Forums

Abstract

Against the backdrop of rapid advances in artificial intelligence, big data, cloud/edge computing, industrial Internet, and digital twins, the industrial sector is undergoing a major transition from automation to intelligence and from experience-driven to data-driven operation. Industrial intelligence can significantly improve production efficiency, product quality stability, and energy utilization, while also enhancing equipment reliability, raising safety levels, and supporting the manufacturing sector’s green and low carbon transformation.
This forum focuses on the frontier developments, theories, and application challenges of industrial intelligence. Distinguished scholars from leading domestic universities will present the latest research in this field and jointly explore how intelligent control and decision making methods can enhance industrial systems’ autonomy, robustness, and safety, providing theoretical support and engineering pathways for the next generation of industrial intelligent transformation.

Chair: Prof. Hua Geng Tsinghua University, China

Biography

Hua Geng received the B.S. degree in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2003 and the Ph.D. degree in control theory and application from Tsinghua University, Beijing, China, in 2008. From 2008 to 2010, he was a Postdoctoral Research Fellow with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada. He joined Automation Department of Tsinghua University in June 2010 and is currently a full professor.
His current research interests include advanced control on power electronics and renewable energy conversion systems, AI for energy systems. He has authored more than 300 technical publications and holds more than 30 issued Chinese/US patents. He was the recipient of IEC 1906 Award, IEEE PELS Sustainable Energy Systems Technical Achievement Award. He is the Editor-in-Chief of IEEE Trans. on Sustainable Energy. He served as general chair, track chairs and session chairs of several IEEE conferences. He is an IEEE Fellow and an IET Fellow, convener of the modeling working group in IEC SC 8A.

Prof. Wenwu Yu Southeast University, China

Abstract

This talk introduces the research field and research process of system science in detail, and then concludes and clarifies that the development of artificial intelligence has promoted the derivation and development of intelligent systems. In addition, this report analyzes in detail the important role of big data in artificial intelligence and intelligent systems, and explains in detail that big data and intelligent systems have become an emerging core direction of system science in the new era. In a closer step, this talk introduces in detail the current bottleneck problems of intelligent systems in big data processing analysis and decision-making and regulation, and puts forward important ideas of using the intelligent thought and technical system of collective intelligence to solve the above challenges and promote the future development of big data and intelligent systems, and point out the direction for the development of future system science.

Biography

Wenwu Yu received the B.Sc. degree in information and computing science and M.Sc. degree in applied mathematics from the Department of Mathematics, Southeast University, Nanjing, China, in 2004 and 2007, respectively, and the Ph.D. degree from the Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China, in 2010. Currently, he is the Dean in the School of Mathematics. He is also a Full Professor with the Endowed Chair Honor in Southeast University, China.
Professor Yu is also the Executive Deputy Director of the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, the Vice Director of the Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education of China, the Head of the Information Mathematics Discipline in the Research Center for Mathematical and Physical Fundamentals, Purple Mountain Laboratories, the Executive Director of the Jiangsu National Center of Applied Mathematics in Southeast University, and the Huawei-Southeast University Networked Collective Intelligence Joint Innovation Lab.
Dr. Yu held several visiting positions in Australia, China, Germany, Italy, the Netherlands, and the USA. His research interests include multi-agent systems, complex networks and systems, disturbance control, distributed optimization, machine learning, game theory, cyberspace security, smart grids, intelligent transportation systems, big-data analysis, etc.
Dr. Yu severs as an Editorial Board Member of several flag journals, including IEEE Transactions on Circuits and Systems II, IEEE Trans. Industrial Cyber-Physical Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Science China Information Sciences, Science China Technological Sciences, etc. He is also the Editor-in-Chief of Complex Engineering Systems.
He was listed by Clarivate Analytics/Thomson Reuters Highly Cited Researchers in Engineering in 2014-2024. He publishes about 100 IEEE Transactions journal papers with more than 20,000 citations. Moreover, Dr. Yu is also the recipient of the Second Prize of State Natural Science Award of China in 2016. He is also the Cheung Kong Scholars Programmer of Ministry of Education of China (Artificial Intelligence).

Prof. Qinglei Hu Beihang University, China

Abstract

Spacecraft proximity operations with non-cooperative targets, as enabling technologies for some current and near-future missions such as removing space debris, repairing defunct satellites, etc., have garnered extensive attention. The success of these missions heavily relies on accurate target perception and safe proximity control. However, the non-cooperative nature of targets and the complexities of the space environment pose significant challenges for the target perception and control of spacecraft proximity operations. In this talk, I would like to share our recent research advances on the intelligent perception and control for spacecraft proximity operations with non-cooperative targets. The main research contents include: 1) intelligent target perception in the complex space environment, including representation and determination of semantic information, three-dimensional reconstruction, and pose measurement of space non-cooperative targets; 2) reinforcement-learning-based intelligent proximity control under complex motion and physical constraints; 3) simulation and experimental validation of the proposed method in typical scenes. The research results provide significant theoretical and technical support for the autonomous manipulation and control of space non-cooperative targets. Finally, I shall close by discussing on-going and future research avenues that can further address some practical engineering problem in spacecraft proximity operations.

Biography

Qinglei Hu obtained his B.Eng. degree in electrical and electronic engineering from Zhengzhou University, Zhengzhou, China, in 2001, and his Ph.D. degree with the specialization in guidance and control from Harbin Institute of Technology, Harbin, China, in 2006. From 2003 to 2014, he was with the Department of Control Science and Engineering, Harbin Institute of Technology, and then he joined Beihang University in 2014 as a Full Professor. His current research interests include intelligent perception and control, fault diagnosis and fault-tolerant control, and their applications in autonomous spacecraft systems. He has published five monographs in Elsevier, Springer, etc.,, and 80+ journal papers in IEEE transactions and AIAA journals. He has authorized 30+ national invention patents. He has won the second prize of national Technological Invention Award and the first prize of national defense technological invention Award. He has been appointed the Changjiang Distinguished Professorship, and has been selected as Thomson Reuters Highly Cited Researchers from 2016-2022. Currently, he serves as an Associate Editor for Aerospace Science and Technology.

Prof. Huaicheng Yan East China University of Science and Technology, China

Abstract

This talk will first introduce the major issues and challenges facing new-type power systems, then present the team's recent advances in intelligent unmanned system technologies, including some latest research progress in distributed positioning estimation and formation control of intelligent unmanned systems in constrained environments, and finally provide an outlook on their applications in new-type power systems.

Biography

Huaicheng Yan received his B.Sc. degree in automatic control from Wuhan University of Technology, China, in 2001, and the Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology, China, in 2007. From 2007 to 2009, he was a Postdoctoral Fellow with the Chinese University of Hong Kong. Currently, he is a Professor with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. He has published more than 300 in top journal of Automatica and IEEE Transactions journals. He is IET Fellow, the Highly Cited Researcher of Clarivate from 2019 to 2025, and also a recipient of Leading Talent of National Ten Thousand Plan. He is an associate editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, SCIENTIA SINICA Information, International Journal of Robotics and Automation and IEEE Open Journal of Circuits and Systems, etc. His research interests include networked control systems, multi-agent systems, robotics and power systems.

Prof. Zhongkui Li Peking University, China

Abstract

Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human–swarm collaboration, translating the operator’s intent into coordinated swarm actions and helping determine when validation or intervention is required during execution.  In long-horizon missions under dynamic scenarios, however, reliable task planning becomes difficult to maintain: emerging events and changing conditions demand continual adaptation, and sustained operator oversight imposes substantial cognitive burden.  Existing LLM-based planning tools can support plan generation, yet they remain susceptible to invalid task orderings and infeasible robot actions, resulting in frequent manual adjustment. Here we introduce a neuro-symbolic framework for long-horizon human–swarm collaboration, which tightly couples verifiable task planning with context-grounded LLM reasoning. We formalize mission goals and operational rules as temporal logic formulas and task automaton for admissible task orderings. Guided by these symbolic certificates and live perceptual context, LLMs generate executable subtask sequences that are consistent by construction. A transparent and uncertainty-aware scheduler then assigns subtasks across the heterogeneous swarm to maximize parallel execution while remaining robust to disruptions. Finally, an event-triggered interaction protocol reduces the operator input to sparse and high-level confirmation or guidance. In large-scale simulations with more than 40 robots executing 41 tasks with 155 subtasks in 11-minute missions, our system improves task success rates by 26% and increases completed tasks by 132% over state-of-the-art baselines. At the same time, it reduces operator interventions by 77% and lowers physiological stress by 44%. We further deploy the software stack on a heterogeneous robotic fleet, obtaining similar results while remaining robust to hardware-specific actuation and communication uncertainties.

Biography

Zhongkui Li received the B.S. degree in space engineering from the National University of Defense Technology, China, in 2005, and his Ph.D. degree in dynamical systems and control from Peking University, China, in 2010. Since 2013, Dr. Li has been with the Peking University, China, where he is currently a Full Professor with the School of Advanced Manufacturing and Robotics. His current research interests include cooperative control and planning of multi-agent systems. Dr. Li was the recipient of the China National Science Funds for Distinguished Young Scientists in 2024, the State Natural Science Award of China in 2015, the Natural Science Award of the Ministry of Education of China in 2022 and 2011, and the National Excellent Doctoral Thesis Award of China in 2012. His coauthored papers received the IET Control Theory \& Applications Premium Award in 2013 and the Best Paper Award of Journal of Systems Science \& Complexity in 2012.

Prof. Dongsheng Yang Northeastern University, China

Abstract

The rapid advancement of distributed power sources, microgrids, smart electricity consumption, electric vehicles, and energy storage has led to a shift in power flow from unidirectional to bidirectional and multidirectional, resulting in increasing complexity in the operation and control of the electric power industry. At the same time, it poses new challenges to the safe operation of power systems. To address this, a novel early fault analysis framework for power equipment is designed by combining data-driven and model-based approaches. A deep adversarial-based model is established to enhance weak features of early faults, making full use of operating condition signals collected under different states and time periods to generate augmented data in an end-to-end manner. An enhanced fault identification algorithm based on anomaly detection is proposed, enabling early fault analysis through degradation enhancement. Furthermore, a theoretical framework for early warning of industrial engineering faults is developed based on model-based and small-sample data theories, providing theoretical support for industrial condition analysis and safe operation.

Biography

Dongsheng Yang , Professor and Doctoral Supervisor, IET Fellow, National High-Level Talent, Recipient of the State Council Special Allowance. He currently serves as Associate Dean of the School of Information Science and Engineering, Northeast University. His main research interests include digitalization of energy systems and artificial intelligence control for the Energy Internet. As Principal Investigator, he has led one key project of the National Natural Science Foundation of China and one project under the National Key Research and Development Program. He serves as a Guideline Expert for the "Clean and Efficient Utilization of Coal Technology" key special project of the National Key Research and Development Program, as well as for the "Smart Grid 2030" guideline expert group.

Abstract

As an important direction in the development plan of China's new generation of artificial intelligence, multimodal intelligent cooperative perception and decision-making is changing the mode of industrial production and social development. Multimodal modeling and decision-making, large models, embodied intelligence, and other technologies will provide new solutions for complex intelligent systems, particularly for industrial production process identification, operating performance assessment, optimization decision-making, monitoring, and fault diagnosis, through deep multimodal information fusion and efficient, dynamic interaction and collaboration with the human-machine environment, thereby enhancing the system's autonomous perception and decision-making capabilities. This forum will gather renowned experts and scholars in multimodal modeling and decision-making, large models, and embodied intelligence. It will focus on the latest theoretical research and technological practices in this field and promote the development of intelligent, green, and high-quality new productivity.

Chair: Prof. Dakuo He Northeastern University, China

Biography

Dakuo He is a professor at Northeastern University and the head of the Innovation Team at Liaoning University. He is currently the Vice Dean of the School of Information Science and Engineering of Northeastern University, a member of the Process Control Special Committee of the Chinese Society of Automation, a member of the Automation Special Committee of the Chinese Society of Non-ferrous Metals, and Deputy Director of the Liaoning Provincial Key Laboratory Cluster of Robotics. He is engaged in research on industrial artificial intelligence and its related disciplinary theories and practical techniques. As the project leader, he has undertaken 5 projects for the National Natural Science Foundation of China, 3 projects for the National Key Research and Development Program, and many cooperative projects of enterprises; as the first completer, he won 1 first prize in Science and Technology Progress of Chinese Association of Automation, and as a key member, he won 1 first, second and third prizes each of Science and Technology Award of China Nonferrous Metals Industry, and 1 first prize of Science and Technology of China Gold Association. He has published more than 100 high-level academic papers, with more than 100 indexed by SCI and EI. He has also participated in the consulting projects of the Chinese Academy of Engineering, and written research reports, including "Research on the development strategy of intelligent optimization manufacturing in the process industries" and "Research on the feasibility and development strategy of genetic mineral processing engineering".

Prof. Chunhui Zhao Zhejiang University, China

Abstract

Battery health management is the core critical element for ensuring the safe, efficient, and long-service-life operation of lithium battery systems. Traditional decision-making models built on lithium battery time series data (including voltage, current, temperature, internal resistance, etc.) usually only output abstract results such as anomaly scores, fault types, and SOX estimates, and fail to address practical questions including "why the battery is abnormal", “where the root cause of the fault lies”, and “how to conduct operation, maintenance (O&M) and repair”. Although large language models (LLMs) have demonstrated tremendous potential in knowledge-driven decision-making, a significant semantic gap emerges when they are directly applied to lithium battery time series signals: the continuous, high-dimensional, and strongly coupled time series data of battery operation is difficult to be effectively encoded into discrete semantic units that are understandable by language models. Different from the traditional “signal-to-value” decision-making paradigm in battery management, this study proposes an interpretable intelligent decision-making framework for lithium battery management, which realizes end-to-end decision-making of “from battery time series signals to natural language semantics” (Signal-to-Semantics, S2S). This study breaks through the traditional paradigm of “time series data → abstract decision-making”, and directly outputs fault reasoning processes, state diagnosis conclusions, and O&M disposal recommendations that are understandable, verifiable, and implementable by battery domain experts. It constructs a highly reliable and interpretable artificial intelligence decision-making method suitable for lithium battery management scenarios, providing a brand-new technical path for battery safety early warning, health assessment, life prediction, and intelligent O&M.

Biography

Chunhui Zhao , Qiushi Distinguished Professor, Recipient of the National Outstanding Youth Fund. She received Ph.D. degree from Northeastern University, China, in 2009. From 2009 to 2012, she was a Postdoctoral Fellow with the Hong Kong University of Science and Technology and the University of California, Santa Barbara, Los Angeles, CA, USA. From 2012 to 2014, she was a distinguished researcher with Zhejiang University and since Dec. 2014, she has been a Professor with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Her research interests are artificial intelligence theory and methods for industrial applications. She has authored or coauthored more than 260 papers in peer-reviewed international journals. She has published 4 monographs and four big data textbooks. She authorized more than 90 invention patents. She is principal investigator of a Distinguished Young Scholar Program supported by the Natural Science Foundation of China. She has hosted more than 20 scientific research projects, including the NSFC funds, National key R&D project, provincial projects and corporate cooperation projects. She has been awarded multiple research accolades, including the First Prize in Natural Science of Zhejiang Province, the Natural Science Award from the Ministry of Education, the inaugural Youth Science and Technology Award of Zhejiang Province, and the First Prize in Natural Science from the Chinese Association of Automation. She has been honored with more than ten academic awards, including the China Young Female Scientist Award, the Fellow of IET, the Model Woman Pacesetter of Zhejiang Province, et al. She has served AE of multiple International Journals, including IEEE Transactions on Automation Science and Engineering, Journal of Emerging and Selected Topics in Industrial Electronics, Journal of Process Control, Control Engineering Practice and Neurocomputing, etc.

Prof. Weimin Zhong East China University of Science and Technology, China

Abstract

This report introduces the recent research directions of the research group, focusing on steady-state and dynamic modeling, performance evaluation, operation optimization, and fault diagnosis of heavy-duty gas turbines, providing methodological and technical support for the operation optimization and regulation of heavy-duty gas turbines.

Biography

Weimin Zhong , recipient of the National Science Fund for Distinguished Young Scholars (Category A). He currently serves as the Dean of the School of Information Science and Engineering at East China University of Science and Technology. His research primarily focuses on industrial intelligence technology and intelligent systems, as well as fundamental theories, methods, and technological development for intelligent manufacturing in process industries. He has led or participated in projects such as the Basic Science Center Project (Principal Investigator), Major Program Projects of the National Natural Science Foundation of China, National Key Research and Development Program projects, and over 20 enterprise technology development projects.

Prof. Hesheng Wang Shanghai Jiao Tong University, China

Abstract

This report focuses on the two core functions of service robots: Navigation and manipulation. First, an overview of the current status of the service robot industry and technological development, along with the challenges faced, is provided. Next, the report introduces the key achievements of the team's long-term efforts in addressing the core technical challenges of Navigation and manipulation. The method for navigation achieves robust perception and localization of mobile robots through visual fusion in complex and large scenes. The framework proposed for manipulation solves the challenge of high-precision robotic operations. A practical and versatile vision-based method system has been established, elevating the critical technological level of service robots.

Biography

Hesheng Wang is a Distinguished Professor and Dean of the Global College, Shanghai Jiao Tong University, China. He has published more than 200 papers in refereed journals and conferences. Dr. Wang is/was an Associate Editor of IEEE Transactions on Robotics, IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, Robotic Intelligence and Automation and the International Journal of Humanoid Robotics, a Senior Editor of the IEEE/ASME Transactions on Mechatronics, an Editor-in-chief of Robot Learning. He was the General Chair of IROS2025, ROBIO 2022 and RCAR 2016.

Prof. Liang Shen Jilin University, China

Abstract

Detectors are core components in fields such as aviation, aerospace, and information technology. Traditional semiconductor-based photodetectors are heavy, rigid, and fragile, making it difficult to achieve lightweight designs. In contrast, novel photodetectors made from organic, perovskite, and quantum dot materials offer advantages such as simple fabrication processes, low costs, and light weight, promising to meet national strategic demands. The presenter has long been engaged in the research of new semiconductor detectors, synthesizing high-quality and stable perovskite single crystals and developing highly sensitive, low-voltage-driven, portable perovskite X-ray detectors. By employing a combined strategy of surface functionalization design and reducing device capacitance, a response speed in the nanosecond range has been achieved. Based on the principle of complementary absorption spectra and the principle of carrier interface transport, a new type of photodetector integrating wide spectral range, fast response, and large dynamic linear range has been developed, enabling near-infrared high-speed scanning imaging. The various high-sensitivity, multi-spectral, fast-response, and large dynamic linear range detectors that have been reported have been applied in optical communication and imaging, achieving precise transmission of optical signals in different spectral bands and high-precision imaging, demonstrating their application potential in aerospace and optoelectronic information fields, and making meaningful explorations towards the practical application of new detectors.

Biography

Liang Shen , a professor at Jilin University, is a doctoral supervisor and recipient of the National Science Fund for Distinguished Young Scholars. He is also a young scholar of the Ministry of Education's major talent program and the vice dean of the School of Electronic Science and Engineering at Jilin University. Relying on the National Key Laboratory of Integrated Optoelectronics, he mainly conducts research on new semiconductor photoelectric conversion materials, devices and systems. His research focuses on exploring the mechanisms of new materials, developing high-performance devices and integrating new application systems. He has achieved significant original results in high-speed optoelectronic theory and devices, low-bias high-sensitivity X-ray detection, and ultra-stable photomultiplier tubes. His work has also made important applications in optical communication, optical interconnection and optical information processing, serving the country's major strategic needs. He has published over 200 SCI-indexed papers in international important academic journals such as Nat. Photon., Nat. Water, Nat. Commun., Sci. Adv., Adv. Mater., Nano Lett., Adv. Funct. Mater., Adv. Energy Mater., and Light-Sci. Appl., with over 12,000 citations (Google Scholar) and an H-index of 62. He has been granted over 30 US and Chinese invention patents. In 2015, he won the First Prize of the Natural Science Award of Jilin Province. Currently, he serves as a young editorial board member of the Journal of Semiconductors, InfoMat, SmartMat, InfoScience, Acta Photonica Sinica, and Laser & Optoelectronics Progress.

Associate Prof. Chao Shang Tsinghua University, China

Abstract

Valves are the most commonly used actuators in the process industry. However, control valve stiction is a frequent occurrence that directly impacts equipment lifespan, production efficiency, and product quality. Detecting valve stiction has long been a challenging problem in loop performance assessment and fault diagnosis. Existing detection methods primarily rely on complex statistical modeling or large volumes of manually annotated data, which suffer from issues such as difficulty in threshold determination, poor transferability, and insufficient interpretability, making them difficult to adapt to the complex and variable conditions of real-world industrial scenarios. This report introduces an intelligent agent, StictionGPT, designed for valve stiction detection, diagnosis, and maintenance tasks. The development of this agent is based on a dual-drive approach combining "visual features and semantic understanding." By aligning industrial knowledge and expert experience with the cognitive capabilities of general large models, it guides multimodal large models to integrate visual shape features and textual semantics for reasoning and judging valve stiction, simulating the comprehensive decision-making process of human experts. Additionally, LoRA fine-tuning is employed to achieve efficient few-shot generalization. StictionGPT has achieved the highest accuracy on the ISDB international benchmark and has demonstrated excellent practical performance in a chemical plant in China.

Biography

Chao Shang is a Tenured Associate Professor at Tsinghua University and serves as the Deputy Secretary of the Party Committee in the Department of Automation. His research focuses on process modeling, control, and optimization driven by industrial big data. His academic achievements have been scaled up and implemented in key national economic sectors such as photovoltaics and oil refining, yielding significant results. He currently holds positions as the Deputy Secretary-General of the Technical Committee on Fault Diagnosis and Safety of Technical Processes and a member of the Technical Committee on Process Control, both under the Chinese Association of Automation. He also serves on the editorial boards of SCI-indexed journals, including Expert Systems with Applications and Control Engineering Practice.
He has independently published one English monograph and authored over 100 papers, with more than 50 published in prestigious control and decision-making journals such as IEEE Transactions on Automatic Control and Automatica, accumulating over 4,000 citations. He was selected for the National "Ten Thousand Youth Talents Plan" and was named an "Emerging Leader" by the IFAC journal Control Engineering Practice. Additionally, he has received a number of awards, including the Springer Excellent Doctorate Thesis Award, Best Paper Awards at multiple academic conferences, and the Tsinghua University Annual Teaching Excellence Award.

Abstract

Facing increasingly complex application scenarios, enhancing the performance of intelligent systems requires breakthroughs in collaborative optimization and advanced computing. This forum focuses on cooperative control of multi-agent systems, distributed optimization algorithms, and frontier theories in intelligent computing. It highlights how model-driven and data-driven approaches can improve the overall performance, adaptability, and intelligence of such systems. The forum aims to share the latest research progress in collaboration and computing for intelligent systems and to promote innovative applications in robotics, intelligent manufacturing, and related fields.

Chair: Prof. Long Jin Lanzhou University, China

Biography

Long Jin is a Professor and Doctoral Supervisor at Lanzhou University, awarded the National-Level Young Talents Program. In 2023, he was a Visiting Professor in the Department of Computer Science at City University of Hong Kong. He has served as PI for four projects from the National Natural Science Foundation of China, and several provincial/ministerial-level projects, including key/outstanding youth projects from the Natural Science Foundation of Gansu Province. He has been consecutively included in Elsevier's list of "Highly Cited Chinese Researchers" and the global Top 0.05% Scholars list for multiple years. His honors include the Excellent Doctoral Dissertation Award from the Chinese Association for Artificial Intelligence (CAAI), the Wu Wenjun Artificial Intelligence Excellent Youth Award, and the Second Prize of the Natural Science Award of Gansu Province. Under his supervision, his students have received numerous awards, including Outstanding Doctoral and Master's Theses from national first-level societies and provincial authorities, as well as the Outstanding Ph.D. Student Project from the National Natural Science Foundation of China. He currently holds associate editor positions for several SCI-indexed journals, including IEEE TIE, TIV, TASE, TFS, JAS, Neural Networks, and CAAI TRIT, and has repeatedly received Outstanding Editor Awards from journals such as IEEE JAS, CAAI TRIT, and IJCAS. His research interests include computational intelligence and applications.

Prof. Zhigang Zeng Huazhong University of Science and Technology, China

Abstract

In the intelligent wave driven by large models, the parameter scale and computing power stacking have significantly enhanced the machine's ability in specific tasks, but it still highly relies on massive data, has high energy consumption, and has insufficient online adaptability to open environments. It still falls short of autonomous intelligence with capabilities such as contextual understanding and emotional interaction. We attempt to take "the four alignments of weak/strong artificial intelligence" as the starting point, review the key correspondences between AI and brain science mechanisms: deep networks and neuronal-synaptic structures, Attention and classical conditioning reflexes, reinforcement learning and operant conditioning reflexes, embodied intelligence and the closed-loop of brain-cerebellum collaboration. Further, it is explained that dynamic associative memory may be an important support for achieving stronger autonomy and adaptability. The report focuses on the operation of associative memory networks based on memristors, analyzes "simulating multi-sensory association" and "simulating emotional generation and evolution". In the future, it may further expand to more rich modalities and higher-level cognitive functions, and integrate with brain-inspired algorithms such as SNN and HTM, as well as memory-and-computation integrated chips, to support the implementation of applications such as autonomous intelligent unmanned systems, emotional robots, and intelligent wearables.

Biography

Zhigang Zeng is the dean of the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology, and the director of the Key Laboratory of Image Information Processing and Intelligent Control of the Ministry of Education. He is also an IEEE Fellow. In June 2003, he obtained a doctoral degree in System Analysis and Integration from Huazhong University of Science and Technology. He has conducted postdoctoral research at the Chinese University of Hong Kong and the University of Science and Technology of China. He has served as an editorial board member of several internationally renowned journals such as IEEE Transactions on Neural Networks, IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Cognitive Computation, Neural Networks, Applied Soft Computing, Acta Automatica Sinica, Control Engineering, System Engineering and Electronics Technology, and Control Theory and Applications. He has received the First Prize of Natural Science of Hubei Province, the First Prize of Science and Technology Progress of Hubei Province, the First Prize of Excellent Scientific Research Achievements of Higher Education Institutions of the Ministry of Education, and the Second Prize of National Science and Technology Progress Award.

Prof. Honggui Han Beijing University of Technology, China

Abstract

Municipal wastewater treatment is an effective way to protect the environment and realize water resource recycling. However, due to the multi-processes, multi-working conditions, time-varying and other characteristics of municipal wastewater treatment process, optimal control based on a single scale, a single level, and a single goal cannot guarantee the optimum of the overall operation. Multi-objective collaborative optimization control achieves multi-objective optimization between local and global, short-term and long-term, and efficiency and safety in the municipal wastewater treatment process by constructing performance indicators at different time scales and designing a multi-conflict objective dynamic optimization method. It solves the problem of real-time dynamic optimization setting of key variables in the municipal wastewater treatment process, and effectively reduces the operating cost.

Biography

Honggui Han , professor, doctoral supervisor, and dean of the School of Computer Science. He has been engaged in research on intelligent control of complex systems, and has been selected for the National Science Fund for Distinguished Young Scholars, the National Science Fund for Excellent Young Scholars, the Young Beijing Scholar, the Young Scientist of the Chinese Automation Society, and the Outstanding Young Scientist of Beijing Universities, etc. As a result of the research, he has published more than 100 academic papers and written 5 books; he has obtained more than 60 authorized Chinese/American invention patents, has presided over/participated in the formulation of more than 10 national/group/local standards. He has won the second prize of the National Science and Technology Progress Award, the first prize of the Ministry of Education Science and Technology Progress Award, and the first prize of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award, etc. He is currently the director of the "Digital Community" Engineering Research Center of the Ministry of Education and the director of the Beijing Key Laboratory of "Computational Intelligence and Intelligent Systems". He also serves as an editorial board member of journals such as China Science: Technical Sciences, IEEE Transactions on Cybernetics, etc.

Prof. Gang Wang Beijing Institute of Technology, China

Abstract

World models provide an efficient "imagination" training space for embodied AI by simulating environmental dynamics. However, existing methods face significant challenges in long-horizon modeling, dynamic perception, multi-task generalization, and sample efficiency. Focusing on efficient world model construction and reasoning, we have made a series of advancements: STORM proposes a Transformer-based decoupled training architecture, reducing the single-task training cost to 6.8 RMB; DyMoDreamer introduces a dynamic modulation mechanism, achieving 156% human normalized score on Atari; Mixture-of-World Models realize unified modeling across 26 games through a modular latent dynamics architecture; Object-Centric World Models enable efficient learning with minimal annotations; and finally, hierarchical value-decomposed offline RL achieves complex task transfer for whole-body control of humanoid robots. These works have been validated in systems such as drones and humanoid robots. Future work will explore general world knowledge learning and inference-time computation scaling.

Biography

Gang Wang is a Professor and Doctoral Supervisor at the School of Automation, Beijing Institute of Technology, where his research focuses on data-driven control of unmanned systems and world model learning. He has served as the Principal Investigator for the National Key R&D Program of China and the Joint Key Program of the National Natural Science Foundation of China. He has published 60 journal papers in top-tier transactions such as IEEE TIT, TAC, and TSP, along with 60 conference papers in leading venues including NeurIPS, ICRA, IROS, and CDC. His accolades include the ICCA Best Paper Award, the IEEE Signal Processing Society’s Outstanding Editorial Board Member Award, the "Best Paper Award from Frontiers of Information Technology & Electronic Engineering, the EUSIPCO Best Student Paper Award, and the Chinese Association of Automation (CAA) Natural Science First Prize. Currently, he serves as an Associate Editor for IEEE Control Systems, IEEE Transactions on Signal and Information Processing over Networks, and IEEE Open Journal of Control Systems, and holds positions as Vice Chair of the CAA Technical Committee on Embodied Intelligence and member of the CAA Technical Committee on Control Theory.

Prof. Bo Yang Shanghai Jiao Tong University, China

Abstract

Electrolysis of water for hydrogen production using new energy can achieve multiple goals, including new energy consumption, carbon reduction, and long-term energy storage. However, the production, transportation, and utilization of green hydrogen are confronted with several challenges: the mismatch between fluctuating green power input and the large-inertia electrochemical process, the spatiotemporal distribution reconstruction of mixed gases in pipelines, and the low-carbon regulation of energy-substance conversion processes. To address these issues, this report first introduces a decision-driven learning-based power allocation scheme for multiple electrolyzers, which ensures consistency between green power prediction and power allocation strategies. Subsequently, a DeepOnet-based method for estimating the spatiotemporal distribution of mixed gases and an AI-embedded multi-energy joint dispatching method are proposed. Furthermore, an optimization-guided joint dispatching strategy for high-energy-consumption production processes and energy is put forward to realize carbon and cost reduction under dynamic and uncertain conditions. Finally, the design method of a green hydrogen energy management and control platform based on AI Agent is presented.

Biography

Bo Yang is a Distinguished Professor at Shanghai Jiao Tong University and currently serves as the Dean of the School of Automation and Intelligent Sensing. He also holds the positions of Vice Chairman of the Shanghai Society of Automation and Director of the Shanghai Engineering Research Center of Industrial Intelligent Control and Management. His research focuses on integrated energy systems and the industrial Internet of Things. He has authored over 200 academic papers and one monograph, and holds 40 granted invention patents, including one U.S. invention patent. As the Principal Investigator (PI), he has led more than 20 major projects at the provincial, ministerial, and national levels, such as the NSFC Young Scientist Fund Project (Class A), NSFC Key Projects, and Key R&D Program Projects of the Ministry of Science and Technology. His research accomplishments have been recognized with numerous prestigious awards, including the Ministry of Education Natural Science Award, the Shanghai Technology Invention Award, the IEEE TCCPS Outstanding Industrial Contribution Award, and the Chinese Association of Automation (CAA) Young Scientist Award. He is also a recipient of the National High-Level Talents Special Support Program (Young Top Talents).

Prof. Dongrui Wu Huazhong University of Science and Technology, China

Abstract

Brain-computer interface (BCI) serves as a direct interaction channel between the brain and external devices (such as computers and robots). Due to individual differences and the non-stationarity of electroencephalogram (EEG) signals, BCI systems generally require personalized calibration for new users or new tasks, which is time - consuming and labor - intensive, and may dampen user interest. Advanced signal processing and machine learning methods can reduce or even completely eliminate the need for calibration, thereby enhancing the system's accuracy and user - friendliness. This report will present the research progress in the precise decoding of BCI, including data alignment, transfer learning, knowledge - data fusion, and large - scale models.

Biography

Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Chair Professor and Vice Dean of School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. His research interests include brain-computer interface and machine learning. He has more than 200 publications (18000+ Google Scholar citations; h=70), with 7 outstanding paper awards. His team won National Champion of the China Brain-Computer Interface Competition in seven successive years (2019-2025). He is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

Prof. Long Jin Lanzhou University, China

Abstract

This presentation investigates the theoretical and practical integration of deep neural networks and neural differential equations to address fundamental challenges in model robustness, trainability, and generalization. By interpreting network depth as time in dynamic systems, the research leverages numerical analysis concepts, specifically zero stability, consistency, and Shannon’s sampling theorem, to guide the design of stable and efficient network architectures. Furthermore, it introduces novel optimization algorithms, such as gradient activation, loss landscape reshaping, and integral-based smoothing, which utilize dynamic system principles to handle ill-conditioned problems, escape saddle points, and converge to flat minima. Collectively, we provide a low-cost, high-efficiency framework for enhancing deep learning performance by bridging discrete network structures with continuous differential equation theory.

Biography

Long Jin is a Professor and Doctoral Supervisor at Lanzhou University, awarded the National-Level Young Talents Program. In 2023, he was a Visiting Professor in the Department of Computer Science at City University of Hong Kong. He has served as PI for four projects from the National Natural Science Foundation of China, and several provincial/ministerial-level projects, including key/outstanding youth projects from the Natural Science Foundation of Gansu Province. He has been consecutively included in Elsevier's list of "Highly Cited Chinese Researchers" and the global Top 0.05% Scholars list for multiple years. His honors include the Excellent Doctoral Dissertation Award from the Chinese Association for Artificial Intelligence (CAAI), the Wu Wenjun Artificial Intelligence Excellent Youth Award, and the Second Prize of the Natural Science Award of Gansu Province. Under his supervision, his students have received numerous awards, including Outstanding Doctoral and Master's Theses from national first-level societies and provincial authorities, as well as the Outstanding Ph.D. Student Project from the National Natural Science Foundation of China. He currently holds associate editor positions for several SCI-indexed journals, including IEEE TIE, TIV, TASE, TFS, JAS, Neural Networks, and CAAI TRIT, and has repeatedly received Outstanding Editor Awards from journals such as IEEE JAS, CAAI TRIT, and IJCAS. His research interests include computational intelligence and applications.

Abstract

Autonomous intelligence represents one of the ultimate goals of artificial intelligence, with its core lying in enabling systems to independently accomplish complex decision-making and control tasks through active perception and interaction with the environment. Achieving this goal urgently requires breakthroughs in key technologies such as dynamic environmental perception, human-machine interaction and collaboration, and autonomous decision-making and planning. This forum focuses on innovative developments in automation technology, addressing the theoretical and practical challenges of integrating perception, learning, and control in autonomous systems operating under uncertain conditions.

Chair: Prof. Erchao Li Lanzhou University of Technology, China

Biography

Erchao Li , Ph.D. in Engineering, Professor, Doctoral Supervisor, Gansu Provincial Leading Talent (First Level), Dean of the School of Automation and Electrical Engineering.He has presided over 3 NSFC projects and over 30 provincial-ministerial projects, winning more than 10 provincial-ministerial teaching and research awards. He has published over 100 papers (60+ indexed by SCI/EI) in journals like IEEE T-IE, TSMC-S and Acta Automatica Sinica, and is a CNKI Top 1% Highly Cited Scholar.He has guided students to win over 10 national awards and a postdoctoral fellow to win a national bronze medal. He has published 2 independent monographs and 1 co-authored textbook, and serves as editorial board member for multiple journals and committee member of professional committees.Main Research Interests: Intelligent optimization theory and applications; environmental perception, modeling and control of intelligent robots; modeling and operation optimization of integrated energy systems.

Prof. Shihua Li Southeast University, China

Abstract

For mechatronic systems, nonlinearities (frictions, backlash, saturation, etc.), complex internal dynamics, time-varying parameters, external disturbances and complex work tasks make control design a very challenging work. On the other hand, safety becomes an utmost concern for modern mechatronic systems. In this talk, we will elaborate an advanced control framework for intelligent mechatronic systems, focusing on refined modeling, disturbance rejection, and safety-critical control. Compared with high gain control and integral control methods, disturbance estimation-based control provides a different way to handle disturbance. Disturbance estimation based robust control method can effectively improve the disturbance rejection ability and ensure the robustness of closed-loop system. Some new research developments and results on this topic will be introduced. Specially we will discuss on various advanced modeling, analysis and disturbance rejection control techniques for mechatronic control systems with considerations of time delay, constraint safety control. Applications to industrial AC servo, port crane systems, and collaborative robot will be presented to validate the effectiveness of the proposed control framework.

Biography

Shihua Li received his bachelor, master, Ph.D. degrees all in Automatic Control from Southeast University, Nanjing, China in 1995, 1998 and 2001, respectively. Since 2001, he has been with School of Automation, Southeast University, where he is a Chief Professor, Jiangsu Specially Appointed Professor, dean of School of Automation. He is the chairman of IEEE IES Nanjing Chapter, Fellow of IEEE, IET, AAIA and CAA. He is also the Director General of Jiangsu Association of Automation. He is an IEEE Distinguished Lecturer. He serves as members of the Technical Committees on System Identification and Adaptive Control, Nonlinear Systems and Control and Variable Structure and Sliding Mode Control of the IEEE CSS and members of the Technical Committees on Electrical Machines, and Motion Control of the IEEE IndustrialElectronics Society. He is a member of the Technical Committee on Control Theory of Chinese Association of Automation. He served or serves as editor or associate editor of IEEE Transactions on Industrial Electronics, International Journal of Robust and Nonlinear Control, IET Control Theory & Applications, Advanced Control for Applications, etc.
His main research interests include modeling and nonlinear control theory with applications to mechatronic systems. He has published 3 monographs, over 300 international journal and conference papers with 38000+ citations (Google Scholar). He is one of Clarivate Analytics Highly Cited Researchers all over the world in 2017-2024. He is a winner of the 6th Nagamori Award in 2020.

Prof. Cailian Chen Shanghai Jiao Tong University, China

Abstract

Generating parameterized CAD models from visual and/or textual inputs is a fundamental challenge in engineering automation. This task requires not only a deep semantic understanding of geometric intent but also precise 3D spatial reasoning regarding sketch plane orientations and extrusion directions. Existing Multimodal Large Language Models (MLLMs) struggle to infer 3D spatial positions within continuous coordinate spaces, resulting in low generative efficiency and significant geometric inaccuracies. In this talk, we propose CAD-GPT, a spatial-reasoning-enhanced MLLM specifically designed for synthesizing parameterized CAD modeling sequences. CAD-GPT supports the generation of complete modeling sequences from either a single image or a natural language description. Our core contribution is the 3D Modeling Space Mechanism, which maps 3D rotation angles of sketch planes and extrusion directions into a 1D language token space. Furthermore, 2D sketch coordinates are discretized into learnable positional tokens and integrated into the base LLM’s vocabulary. This representation transforms spatial reasoning into a standard next-token prediction problem, fundamentally eliminating the instability inherent in continuous regression head designs. CAD-GPT achieves SOTA performance across tasks such as img2CAD and text2CAD. To validate its end-to-end efficacy in real-world engineering scenarios, we integrated CAD-GPT as the central synthesis engine into NeuroCAD, a multi-agent CAD automation platform. NeuroCAD encompasses specialized agents for 2D drawing parsing, dimension extraction, topological analysis, and modeling planning. We demonstrate the practical utility of this system through a series of industrial implementations and applications.

Biography

Cailian Chen is currently a Distinguished Professor in School of Automation and Intelligent Sensing, Shanghai Jiao Tong University. Her research interests include industrial Internet and Industrial Intelligence. She received the prestigious IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2008, the IEEE TCCPS Industrial Technical Excellence Award in 2022, and five conference best paper awards. She was awarded the N2Women Star in Computer Networking and Communications in 2022. She won the Second Prize of National Natural Science Award from the State Council of China in 2018, the First Prize of Natural Science Award from the Ministry of Education of China in 2006 and 2016, respectively; and the First Prize of Technological Invention of Shanghai Municipal, China, in 2017 and 2023, respectively. She was honored with the “National Outstanding Young Researcher” by NSF of China in 2020, the “Changjiang Young Scholar” in 2015, and the prestigious China Young Women Scientists Award in 2024. She has been actively involved in various professional services. She is a Distinguished Lecturer of IEEE VTS. She serves as the Deputy Editor for National Science Open and Artificial Intelligence for Engineering (Wiley), and an Associate Editor for IEEE Transactions on Vehicular Technology and IET Cyber-Physical Systems: Theory and Applications.

Prof. Guibin Bian Institute of Automation, Chinese Academy of Sciences, China

Abstract

Microscopic surgical robots represent an integrated advancement in precision surgery and intelligent control. They assist surgeons in a variety of microsurgical procedures by enabling highly accurate, dexterous, and standardized operations, and constitute a significant branch of medical surgical robotics. Focusing on microscopic ophthalmic surgery, this lecture reviews the historical development and leading advances in the field, and describes how innovations in surgical microscopes and instruments have catalyzed progress. The next generation of microscopic ophthalmic surgical robots is characterized by greater operational autonomy and flexibility, higher control precision, more comprehensive assistive functions, and improved postoperative feedback. The talk details several key enabling technologies, including the development of intelligent surgical instruments, intraoperative real‑time multimodal navigation methods, robotic intelligent control strategies, and automated surgical assessment. The lecture concludes with an outlook on future opportunities and challenges for microscopic ophthalmic surgical robotics.

Biography

Guibin Bian is a Professor at the Institute of Automation, Chinese Academy of Sciences, and a nationally recognized leading talent. His research focuses on intelligent surgical robotics. He has led projects including a National Key R&D Program, the National Natural Science Foundation’s Major Scientific Instrument Development project, joint foundation projects, and an Innovation & Interdisciplinary Team project of the Chinese Academy of Sciences. He has authored over 190 high‑quality academic papers, 99 of which are indexed by SCI, and received seven international paper awards. He holds 72 granted domestic and international invention patents and participated in drafting one national standard. His honors include the First Prize of the China Instrument and Control Society’s Technological Invention Award, the First Prize of the China Invention Association’s Invention & Innovation Award, the “Strengthening‑Nation Young Scientist” nomination from the Communist Youth League and China Youth Daily, and the Robot Science Leading Award. He serves on expert panels for the 14th Five‑Year National Key R&D Program (Special Projects on “Fundamental Research Infrastructure and Major Scientific Instruments and Equipment Development” and “Intelligent Robotics”), and is a member of the expert working group for the AI domain guideline of the “Strategic Science & Technology Innovation Cooperation” key program. He is President of the Chinese Academy of Sciences Youth Innovation Promotion Association, a member of the Academic Committee of the PLA Key Laboratory for Combat Injury Specialized Treatment, and an editorial board member of IEEE Transactions on Instrumentation and Measurement (TIM), IEEE Transactions on Automation Science and Engineering (TASE), and The Innovation. He has been recognized as an Outstanding Member of the CAS Youth Innovation Promotion Association, Beijing Outstanding Young Scientist, and Beijing Science & Technology Rising Star.

Prof. Xiuling Liu Hebei University, China

Abstract

The brain and the heart are closely linked, and there are many interactive channels between them. The mental tension and emotional excitement can increase the incidence of organic heart disease, especially ischemic heart disease patients with malignant arrhythmia and sudden cardiac death. Meanwhile the abnormal function of the heart will affect the brain's high-level nerve center response, leading to the occurrence of brain diseases. Brain disease and cardiopathy prevention and treatment has long been a key national concern. In recent years, although a series of research advances have been made in their prevention and treatment, problems such as untimely and imprecise diagnosis still exist. This report will focus on the auxiliary diagnostic methods for cardiovascular and cerebrovascular diseases with brain-heart coupling, reveal the coupling law between brain and heart from multiple angles, and provide new ideas for joint brain-heart research. At the same time, it will introduce the research work of the group in auxiliary diagnosis of cardiovascular and cerebrovascular diseases, research and development of hardware equipment, and popularization and application of related technologies.

Biography

Xiuling Liu is a professor and doctoral supervisor at Hebei University, where she also serves as a standing committee member of the Party Committee and vice president. She has been recognized as a leading talent in technological innovation under the national "Ten Thousand Talents Program." Her honors include the National Women's Meritorious Service Award, selection for Hebei Province's "333 Talent Project" (first level), and designation as a Distinguished Young Scholar in Hebei. She also holds the title of Special Government Allowances expert in Hebei Province, has been named one of the province's "Most Beautiful Scientific and Technological Workers," and is listed among the top 100 innovative talents in Hebei universities. She serves on the 10th National Committee of the China Association for Science and Technology (CAST), on the standing committee of the Hebei Provincial Association for Science and Technology, and as secretary-general of the Brain-Computer Interface and Brain-Computer Systems Committee within the Chinese Association of Automation. Her research bridges medicine and engineering, with a focus on brain-computer intelligence for rehabilitation, wearable smart medical devices, and intelligent diagnosis of cardiovascular diseases. She has led numerous national research projects, including those supported by the National Key Research and Development Program, the National Natural Science Foundation of China (major instrument development, key projects, original explorations, and general programs), and innovative research initiatives from the military science and technology commission. She is also committed to translating research into practical applications. Her work has earned her three second-class prizes in the Hebei Provincial Science and Technology Progress Awards and the Hebei Youth Science and Technology Award. Her research team has been named the "Most Noteworthy Scientific and Technological Innovation Team" by Hebei Province and has received the "National Women's Civilization Post" award from the All-China Women's Federation.

Prof. Jinpeng Yu Qingdao University, China

Abstract

Manufacturing is the backbone of the national economy, serving as the foundation for national development, the engine for national prosperity, and the cornerstone for national strength. With the implementation and advancement of “Made in China 2025,” intelligent manufacturing has become a crucial pathway for the transformation and upgrading of China's manufacturing sector as well as a leading force in international competition. High-precision AC drive control systems represent core technologies in smart manufacturing, serving as the “brain” for advanced equipment such as robots. This report focuses on AC drive systems in engineering applications, presenting a series of studies addressing challenges such as the complex dynamic characteristics, the physical constraints of real-world operating conditions, and the difficulties of algorithmic engineering implementation. The research explored intelligent control methods for AC motors, multi-motor synchronous servo systems, and multi-joint robotic systems, along with relevant application cases.

Biography

Jinpeng Yu is a Chang Jiang Scholar Distinguished Professor appointed by the Ministry of Education, a National Outstanding Educator, Chief Professor and Dean of the School of Automation at Qingdao University, a recipient of the Shandong Province May 1st Labor Medal and Provincial Teaching Master, a Highly Cited Scientist globally, a Highly Cited Scholar in China, a Top 0.5% Scientist worldwide, the inaugural Provincial Top Ten Graduate Advisor, and a Provincial Outstanding Science and Technology Worker. He currently serves as Director of the Shandong Provincial Key Laboratory of Industrial Control Technology, Vice President of the Shandong Automation Society, Director of the Provincial Complex Systems and Intelligent Control University Laboratory, Director of the Teaching Committee of the Shandong Automation Society, Council Member and Teaching Committee Member of the Chinese Association of Automation. He is Editor-in-Chief of the distinguished cluster journal Complex Systems and Complexity Science, and serves on the editorial boards of 12 SCI-indexed journals including IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cybernetics. He has authored three monographs as the primary/corresponding author, published over 100 papers in journals such as IEEE Transactions on Automatic Control, and holds more than 40 authorized invention patents. He has led over 30 major projects, including the National Key Research and Development Program and key projects of the National Natural Science Foundation. He was the first recipient of the Second Prize of the National Graduate Teaching Achievement Award and the First Prize of Shandong Provincial Technological Invention Award. His research focuses on intelligent control and robotics, as well as motion control and servo systems.

Prof. Erchao Li Lanzhou University of Technology, China

Abstract

With the continuous advancement of urban wastewater treatment technologies, particularly in the areas of data acquisition, modeling analysis, and control optimization, intelligent monitoring and optimization methods have become key technologies for improving the efficiency, stability, and adaptability of wastewater treatment systems under variable operating conditions. However, soft-sensing modeling in urban wastewater treatment faces multiple challenges. In addition to missing data, issues such as the scarcity of samples under extreme operating conditions and the complex data characteristics in noisy environments significantly impact data accuracy and model generalization ability. Therefore, developing robust and adaptable soft-sensing models that can cope with dynamic changes and non-stationary operating conditions, particularly under complex disturbances, seasonal fluctuations, and changing operating conditions, has become a critical issue to address.
This report primarily explores soft-sensing modeling methods and their associated pre-processing challenges for urban wastewater treatment processes, with a focus on data governance techniques such as missing value imputation and sample augmentation. It also addresses how to improve model robustness and adaptability in multi-task environments. The report first reviews the background and challenges related to data quality issues in wastewater treatment. Next, it introduces several innovative methods to address challenges in modeling wastewater treatment monitoring data, including missing data, non-linear temporal characteristics, and high-noise environments. These methods include a physics-constrained imputation network, an evolutionary optimization-driven multi-graph structure imputation model, and a virtual sample generation framework. Subsequently, the report summarizes how to enhance the robustness and adaptability of soft-sensing models in complex dynamic environments using evolutionary optimization frameworks and ensemble modeling strategies, particularly in the context of multi-operating conditions, multi-scale variations, and data disturbances. Finally, the report discusses the application prospects and technical challenges of these methods in actual wastewater treatment processes, particularly the difficulties in technology transfer during practical deployment and engineering applications, and provides specific guidance for future technological development and engineering implementation.

Biography

Erchao Li , Ph.D. in Engineering, Professor, Doctoral Supervisor, Gansu Provincial Leading Talent (First Level), Dean of the School of Automation and Electrical Engineering.He has presided over 3 NSFC projects and over 30 provincial-ministerial projects, winning more than 10 provincial-ministerial teaching and research awards. He has published over 100 papers (60+ indexed by SCI/EI) in journals like IEEE T-IE, TSMC-S and Acta Automatica Sinica, and is a CNKI Top 1% Highly Cited Scholar.He has guided students to win over 10 national awards and a postdoctoral fellow to win a national bronze medal. He has published 2 independent monographs and 1 co-authored textbook, and serves as editorial board member for multiple journals and committee member of professional committees.Main Research Interests: Intelligent optimization theory and applications; environmental perception, modeling and control of intelligent robots; modeling and operation optimization of integrated energy systems.

Abstract

To lead the new era of smart healthcare in our country and draw up a new blueprint for a healthy China, the "High Forum on Knowledge and Data-driven Smart Diagnosis and Treatment" has emerged. As a top-level industry event in China, this forum adheres to the highest standards and levels, inviting academicians, national young outstanding researchers, Changjiang Scholars, and top medical institution leaders to jointly celebrate this grand occasion. The forum focuses on the two core engines of "knowledge-driven" and "data-driven", deeply discussing their integration and application in the fields of diseases and health. What is particularly distinctive is that this forum uniquely sets up the "Frontier Topic of Knowledge and Data-driven Smart Diagnosis and Treatment", aiming to apply the latest theories of artificial intelligence, modern signal processing, intelligent decision-making and feedback mechanisms to the entire diagnosis and treatment process - from dynamic prediction of diseases based on models, intelligent drug dosage regulation, to personalized rehabilitation robots and closed-loop neural regulation systems, promoting the diagnosis and treatment model to develop from passive static to active, dynamic, closed-loop intervention in a full-cycle, leapfrog, and integrated manner. This ideological collision and multidisciplinary exchange will strive to solve major clinical challenges, stimulate original technological breakthroughs, and provide core driving force and strategic support for building a globally influential smart diagnosis and treatment system.

Chair: Prof. Guanglin Li Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Biography

Prof. Guanglin Li , Ph.D., Senior Researcher (Level 2), is the director of the Integrated Technology Institute at the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, the director of the Key Laboratory of Human-Machine Intelligent Synergy System of the Chinese Academy of Sciences, and the director of the Joint Laboratory of Human-Machine Intelligent Synergy System in Guangdong and Hong Kong. He is also a postdoctoral researcher at the University of Illinois at Chicago in the United States, a senior research scientist at BiotechPlex Biotechnology Company in the United States, an assistant professor at Northwestern University in the United States, and a senior research scientist at the Rehabilitation Research Institute in Chicago, USA. His main research fields include neural rehabilitation engineering, medical devices and intelligent rehabilitation robots, human-machine intelligent enhancement and interaction, etc. As the project leader, he has undertaken several key projects of the National Natural Science Foundation of China, major scientific research instrument development projects, 973 projects and 863 projects. He has published over 120 SCI/EI papers, including more than 80 SCI papers, in journals such as JAMA, Advanced Materials, IEEE Trans, etc. He has also obtained or applied over 80 domestic and foreign.

Prof. Yuanting Zhang The Chinese University of Hong Kong, China

Abstract

Health engineering is an emerging interdisciplinary field that promotes the transformation of the medical model from "disease-centered" to "health-centered". This report introduces two advancements of the team in the field of health engineering: one is the continuous measurement technology of blood pressure graph (TAG), which breaks through the limitations of traditional cuff-based methods and enables non-invasive, real-time, and continuous monitoring of blood pressure waveforms, providing an important means for dynamic capture of cardiovascular changes; the other is the personalized intelligent doctor Dr.PAI based on continuous physiological signals and artificial intelligence, which can achieve early warning, precise classification and personalized intervention of cardiovascular risks. The report aims to demonstrate the technical path from precise perception to intelligent decision-making, and explore the possibility of health engineering empowering future personalized health management.

Biography

Yuanting Zhang is the Founder and Chairman of the Hong Kong Institute of Medical Engineering and LianGan Medical Technology Co , Ltd. He serves as Adjunct Professor at The Chinese University of Hong Kong, Visiting Professor at Oxford University's Institute for Advanced Study (OSCAR), Chief Scientist at Guangdong Medical University, Research Advisor at Massachusetts General Hospital of Harvard Medical School, and Founding Chair and Director of the Cardiovascular and Cerebrovascular Health Engineering Research Center under Hong Kong's InnoHK Innovation Platform. He is an internationally recognized leader in biomedical and health engineering, and has been elected Fellow of IAMBE, AIIA, AIMBE, APAIA, and HKIE, as well as IEEE Life Fellow. He is currently Editor-in-Chief of Progress in Biomedical Engineering and Chair of the IEEE 1708 Working Group on cuffless blood pressure measurement devices.
Professor Zhang has previously held positions at Apple, Karolinska Institute, the Chinese Academy of Sciences, City University of Hong Kong, and Shandong University, and taught at The Chinese University of Hong Kong, where he helped establish three biomedical engineering degree programmes. He has organized or co-chaired nearly 100 international conferences and workshops, delivered more than 300 academic presentations worldwide, and served in major editorial leadership roles, including Founding Editor-in-Chief of the IEEE Journal of Biomedical and Health Informatics. He has led major national and international research projects, has been named for many consecutive years among Elsevier's "Most Cited Chinese Researchers," and has been listed by Stanford University among the world's top 2% scientists in biomedical engineering. He has applied for 125 patents and received more than 30 university, national, and international awards for his contributions to wearable technology, health engineering, and cuffless blood pressure measurement.

Prof. Qinghu Meng Southern University of Science and Technology, China

Abstract

At the forefront of artificial intelligence and bionic robots, how can smart healthcare, especially medical surgical robots, seize the opportunity and stand firm on the crest of the wave? Based on the speaker's over 30 years of innovative experience and learning insights in the field, this lecture reviews the past and gains new knowledge, exploring how smart healthcare can maximize the benefits brought by artificial intelligence and bionic robots, and then looking forward to the future development trends of smart healthcare and medical surgical robots, as well as the opportunities and strategic tactics for the innovative development of clinical medical staff.

Biography

Qinghu Meng , the Chair Professor and Head of the Department of Electronic and Electrical Engineering at Southern University of Science and Technology, is a Fellow of the Royal Society of Canada and IEEE, and a Distinguished Talent of Shenzhen. He previously served as a tenured full professor at the University of Alberta in Canada and as a professor and head of the Department of Electronic Engineering at The Chinese University of Hong Kong. His research interests include robot perception and intelligence. He has led several internationally leading research projects. He has been included in the Stanford University list of the world's top 2% most influential scientists for both lifetime impact and 2024 annual ranking. He has published nearly a thousand papers, filed over 100 domestic and international patents, and delivered over 200 keynote speeches at conferences. He has led more than 60 research projects with a total funding of nearly 100 million yuan. He has won over 30 international and domestic awards, including the Harashima Prize, the highest award in the field of intelligent robots and systems. He serves as the editor-in-chief and editorial board member of several journals, including the founding editor-in-chief of Elsevier's "Biomimetic Intelligence and Robotics", a top-tier English journal, and has served as the chair of many international academic conferences, including the IEEE/RSJ IROS 2005 and IEEE ICRA 2021 conferences, which are flagship events in the field of robotics and automation.

Prof. Hongliang Ren The Chinese University of Hong Kong, China

Abstract

Minimally Invasive Surgeries (MIS) emerging in modern medical treatment have brought new opportunities and challenges for procedure-specific surgical motion generation and the associated motion understanding, which are the foundation of intelligent robotic manipulation and guiding interventions. Image-guided robotic surgery is expected to increase the precision, flexibility, and repeatability of surgical procedures but poses challenges for system development. This talk will highlight our recent developments in dexterous robotic motion generation with motion perception towards intelligent image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using continuum motion generation mechanisms with variable stiffness and context awareness.

Biography

Hongliang Ren received his Ph.D. in Electronic Engineering (Specialized in Biomedical Engineering) from The Chinese University of Hong Kong (CUHK) in 2008. He has served as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC). He has navigated his academic journey through Chinese University of Hong Kong, Johns Hopkins University, Children’s Hospital Boston, Harvard Medical School, Children’s National Medical Center, United States, and National University of Singapore (NUS). His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of the National Science Fund for Distinguished Young Scholars (Category A), CUHK Young Researcher Award, NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, ICBHI Young Investigator Award 2019, and Health Longevity Catalyst Award 2022 by NAM & RGC, Best Paper Awards in IEEE-ROBIO (2019 & 2013), IEEE-RCAR2016, IEEE-CCECE2015, IEEE-Cyber2014 among 30+ other prestigious awards.He has been constantly listed among the world’s top 2% of the most-cited scientists by Stanford University in the career-long category.

Prof. Yang Chen Southeast University, China

Abstract

The report is titled "Intelligent Medical Imaging and Processing", focusing on the feature-based high-quality imaging technologies in intelligent medical imaging driven by clinical tasks, the research and embedding of core algorithms for domestic medical imaging equipment, and clinical task-driven medical imaging processing. It mainly covers four sections: intelligent medical imaging, application of imaging algorithms, intelligent imaging processing and its applications, and cross-disciplinary research thoughts in medicine and engineering.

Biography

Yang Chen Engaging in scientific research related to medical imaging algorithms and intelligent image analysis, serving domestic high-end medical equipment, and publishing over a hundred papers, he is one of the 2022 Chinese High-Cited Scholars listed by Elsevier. Currently, he is a professor at the School of Computer Science and Engineering of Southeast University, a recipient of the National Outstanding Youth Science Foundation, and the principal investigator of a key research project of the Ministry of Science and Technology.

Prof. Long Meng Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Abstract

Classical physical means such as light, sound, electricity and magnetism are not only the core driving force for the paradigm shift in physics, but also the key bridge connecting macroscopic observation and microscopic exploration. In 1986, American scientist Arthur Ashkin used the optical radiation pressure formed by a strong gradient laser to achieve non-contact capture of living cells, opening up a new direction for the manipulation of microscopic biological particles by classical physical fields. Analogous to optical tweezers, ultrasonic manipulation, as another classical physical field manipulation technology, has gradually become a research hotspot in the field of biomedicine in recent years. Our research group has been deeply engaged in the field of microscale ultrasonic manipulation and has made a series of breakthroughs in addressing core bottlenecks such as the difficulty in precise control of microscale acoustic fields and low manipulation efficiency: at the level of acoustic field construction, we have developed a local acoustic field regulation method based on micro-nano array transducers, achieving high-precision manipulation of micrometer and even nanometer-sized biological particles; at the level of cell function analysis, we have developed a technology for precisely inducing cell deformation in time and space by ultrasonic radiation force, which can quantitatively measure the elastic mechanical properties of cells in high throughput and reveal the functional mechanism of cells from a mechanical perspective; at the clinical translation level, we have overcome the problem of precise focusing of ultrasound in complex multi-layer media and successfully developed a wearable ultrasonic neural modulation instrument for the neural intervention of brain diseases such as epilepsy. The above research not only expands the functional boundaries of ultrasonic manipulation technology, but also builds a cross-scale regulation system of "physical field - biological cells - neural function", providing core technical support for the integrated development of biomedical ultrasound and brain-computer interfaces, and is expected to become an innovative tool for early diagnosis and precise treatment of diseases.

Biography

Long Meng a doctoral supervisor and researcher, is the recipient of the National Natural Science Foundation of China's Class A Project for Young Scientists. Currently, he serves as the executive deputy director of the Institute of Biomedical Engineering at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and the director of the Brain-Computer Interface Research Center. He obtained his Ph.D. from the University of Chinese Academy of Sciences in 2012, under the guidance of Academician Zheng Hairong. His research mainly focuses on biomedical ultrasound and ultrasound brain-computer interfaces, and he has achieved systematic and innovative results in the construction of sound fields in complex media, the regulation of ultrasonic radiation force, and their biomedical applications. He is a council member of the Chinese Acoustical Society, and the vice chairperson of the Physical Acoustics Branch and the Biomedical Ultrasound Engineering Branch of the Chinese Acoustical Society. He is also a member of the editorial board of Ultrasonics. He has published over 90 SCI papers in journals such as Science Advances, Nature Chemical Biology, and the Journal of the Acoustical Society of America. He has been granted 25 invention patents, including 5 in the United States, and 8 of them have been industrialized. He has led major national science and technology projects (as the chief), major scientific research instrument development projects of the National Natural Science Foundation of China, and Class A and B projects of the National Natural Science Foundation of China for Young Scientists. He has won the First Prize of Technological Invention of Guangdong Province and the First Prize of Natural Science of Shenzhen City.

Prof. Hongru Li Northeastern University, China

Abstract

Diabetes and its complications pose a major global public health challenge, as traditional screening-based risk management fails to enable early identification and precise intervention. Artificial intelligence (AI) offers a new paradigm for diabetic complication risk management, with wide applications in screening, diagnosis, risk stratification, and prognosis, showing great potential in improving risk detection and clinical decision-making. Our team has developed CGM-based complication risk assessment and multi-modal intelligent diagnostic methods, expanding from electronic medical records to multi-center and multi-source data, achieving advances from single metabolic characterization to multi-source clinical information fusion. In terms of technical evolution, AI-driven risk assessment has moved beyond traditional machine learning, and now extended to multi-modal foundation models, generative AI, and large language models——these technological upgrades have effectively supported the analysis of unstructured medical data and provided strong assistance for intelligent clinical decision-making. While AI has shown great potential in managing the risks of diabetic complications, we must also acknowledge the challenges in its clinical application, such as heterogeneous data, insufficient external validation, and limited model interpretability. Looking ahead, our research will focus on multi-modal collaboration, agent-assisted decision-making, and closed-loop intervention recommendations, aiming to promote diabetes management from simple risk prediction to more individualized, refined, and intelligent intervention practices.

Biography

Hongru Li is a Professor and Doctoral Supervisor at the College of Information Science and Engineering, Northeastern University, China, where he concurrently serves as the Dean of the College and Director of the Institute of Intelligent Technology and Application. He holds the position of Standing Director of the Education Evaluation Professional Committee of the China Society of Educational Development Strategy, and serves as Chairman of the Liaoning Provincial Artificial Intelligence Society, and Director of both the Chinese Association for Artificial Intelligence and the Chinese Association of Automation. A recipient of the Special Government Allowance from the State Council of China, he is also recognized as a Distinguished Teacher of Liaoning Province.
Professor Li's primary research interests lie in artificial intelligence-driven precision medicine and health management, as well as the full life cycle management and predictive maintenance of industrial equipment. He has published over 110 SCI-indexed academic papers as the first or corresponding author in prestigious venues including IEEE Transactions, and has authored 5 monographs. He has successfully led and completed 39 major research projects, supported by the National Natural Science Foundation of China, the National Key R&D Program of China, provincial and ministerial key scientific and technological initiatives, and enterprise-sponsored research programs.

Abstract

This forum focus on cooperative control and intelligent decision-making technologies—the core engine underpinning the safe, efficient, and low-carbon operation of complex energy systems. Currently, the new energy-dominated power grid structure, the penetration of massive power electronic devices, and the demand for multi-dimensional interactions among generation, grid, load, and energy storage (source-grid-load-storage) have posed disruptive challenges to traditional control paradigms and operational management.
We will conduct an in-depth exploration of how cutting-edge technologies reshape the system landscape: Developing grid-forming control, virtual synchronous generator (VSG), and other novel intelligent algorithms to support the stable integration of high-penetration renewable energy; Investigating high-precision modeling, real-time simulation, and distributed autonomous cooperative control architectures for power-electronicized systems; Constructing cloud/edge computing platforms, energy internet of things (Energy IoT), and data governance systems that enable full-domain perception and intelligent decision-making; Tackling key technologies in multi-space-time cooperative optimization of source-grid-load-storage and intelligent dispatch of virtual power plants (VPPs); Promoting innovative applications of artificial intelligence (AI) in state assessment, fault diagnosis, and resilience enhancement; Empowering distribution network digitalization and flexible interaction with user-side resources; Building resilient defense and proactive security systems to address extreme events. The integration of these technologies marks a paradigm shift in energy systems—from passive response to proactive prediction, autonomous coordination, and intelligent decision-making—driving the formation of an efficient, resilient, and low-carbon closed-loop control system.
This forum sincerely invites experts to present cutting-edge achievements in theoretical breakthroughs, algorithmic innovation, engineering validation, and multi-energy system cooperative design. We aim to spark new ideas that lead the energy industry through ideological collisions, thereby accelerating the construction of a future energy system driven by digitalization and intelligence.

Chair: Prof. Jinhai Liu Northeastern University, China

Biography

Jinhai Liu is a Professor at Northeastern University and serves as the Deputy Director of the Institute of Intelligent Electrical Science and Technology. He is also recognized as a Scientific and Technological Innovation Leader under the "Xing Liao Talent Program." He has long been engaged in research on intelligent pipeline inspection and safe operation. The technologies and systems he has developed are widely applied across 25 provinces in China, as well as in the Middle East and South Africa, serving major clients such as CNOOC, Sinopec, CNPC, and PipeChina.
His research interests include industrial intelligence, special-purpose robotics, and electromagnetic non-destructive testing technologies. He has received over 10 scientific and technological awards, including the Second Prize of the State Scientific and Technological Progress Award. He has published more than 100 papers indexed by SCI and holds over 90 authorized invention patents. He serves as a review expert for the National Natural Science Foundation of China and a panel expert for the Ministry of Science and Technology. He has led over 30 research projects commissioned by national, provincial, municipal, and corporate entities, including key and major projects funded by the National Natural Science Foundation of China, key topics within the National Key R&D Program, major sub-topics of the 863 Program, and significant enterprise research initiatives.

Chair: Prof. Yanhong Luo Northeastern University, China

Biography

Yanhong Luo , Ph.D. Supervisor National High-level Young Talent. She is a Director of the IEEE PES (China) Smart Grid and Artificial Intelligence Subcommittee and served as Vice Chair of the IEEE Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning (2015–2016). She has published more than 130 academic papers, including 8 ESI Highly Cited Papers and over 70 papers indexed by SCI. She has obtained more than 20 authorized national invention patents.
Her long-term research focuses on adaptive dynamic programming, distributed optimal control of energy internet, high-penetration integration of distributed energy resources, and aggregation and optimal dispatch of virtual power plants. She has undertaken 5 projects funded by the National Natural Science Foundation of China and the National Key R&D Program, as well as more than 20 horizontal research projects from the Ministry of Education and large power enterprises.
She has received many prestigious awards, including the 2015 IEEE SMC Andrew P. Sage Best Transactions Paper Award, the 2022 Best Paper Award of the Guidance, Navigation and Control International Journal, the 2020 Second-Class National Natural Science Award (Ranked 2nd), the 2017 First-Class Natural Science Award of the Chinese Association of Automation, the 2020 First-Class Science and Technology Progress Award of the Chinese Association of Automation, the 2015 First-Class Natural Science Award of Liaoning Province, and the 2025 Gold Medal at the Geneva International Exhibition of Inventions.

Prof. Alian Chen Shandong University, China, China

Abstract

With the rising penetration of renewable energy, the low inertia and weak support challenges in power systems have grown increasingly prominent. As a core approach to addressing the above bottleneck, grid-forming control technology has gradually stepped from laboratory to engineering application. The operating characteristics of grid-following and grid-forming converters are complementary, and their coordinated control serves as a key technology to ensure the stable, reliable and flexible operation of new power systems. Based on the actual engineering scenarios of new energy stations, this report systematically elaborates the operating mechanisms of the two types of converters, the configuration schemes of hybrid system and the coordinated control framework, analyzes the system stability mechanisms under different control modes, summarizes the research status of mode switching and coordinated control strategies, and prospects the key future technology directions, so as to provide theoretical and technical references for the construction of high-reliability new energy stations with active support capability.

Biography

Alian Chen is a professor and Changjiang Distinguished Professor at Shandong University. Her research primarily focuses on power conversion and control technologies for renewable energy systems, with specialized expertise in photovoltaic generation, energy storage systems, microgrids, and energy routers. She has authored or co-authored over 170 papers in academic journals and international conference proceedings, and has published two monographs. She has granted more than 40 national invention patents. She has been awarded two second prizes of National Science and Technology Progress award and 6 provincial and ministerial science and technology awards. Prof. Chen serves as an Associate Editor for Nature Paternal Journal Power Electronics, Proceedings of the CSEE, Chinese Journal of Electrical Engineering, Journal of Power Supply.

Prof. Yao Sun Central South University, China

Abstract

With the increasing penetration of power electronic converters, modern power systems are evolving toward power-electronics-dominated power systems. In this context, synchronization control is essential for ensuring stable interaction among converters and between converters and the grid. This presentation focuses on the concept and mechanisms of self-synchronizing control in such systems. It first introduces the fundamental principles of self-synchronization and its role in maintaining system stability. From a unified perspective, the presentation then examines self-synchronizing behavior in different configurations, including parallel, series, and hybrid power-electronics-dominated power systems. Typical control principles and representative implementation approaches are discussed to illustrate the feasibility and advantages of self-synchronizing operation.

Biography

Yao Sun , Ph.D., Professor at Central South University. His research interests include modeling and stability analysis of power electronic equipment, and new type power system. He has published over 200 papers in SCI indexed journals, and has been consecutively listed among Elsevier’s Highly Cited Chinese Researchers for five years. He has received 5 provincial/ministerial-level science and technology awards, including the First Prize of the Hunan Provincial Natural Science Award.

Prof. Wangli He East China University of Science and Technology, China

Abstract

Hydrogen holds a strategic supporting role in building a clean, low-carbon, safe and efficient energy system. The integration of green electricity and green hydrogen with green chemical production is driving the deep decarbonization transition of the industry through technological innovation and system optimization. Wind-solar-hydrogen-storage low-carbon energy systems have become a critical implementation pillar for China’s energy security and the “Dual Carbon” strategy. The large-scale and rapid deployment of variable renewable energy, heterogeneous energy storage, and complex market factors pose fundamental academic challenges to the research on optimal operation and control of power-hydrogen integrated energy systems. This report presents preliminary explorations on multi-scale mechanism modeling of electrolyzers (the key equipment for renewable hydrogen production), system capacity configuration and scheduling under wind-solar uncertainty, and peer-to-peer energy trading. It aims to establish the chain of “underlying modeling—cross-domain trading—intra-domain scheduling”, advance the deep integration of renewable energy into the energy system restructuring via power-hydrogen fusion innovation, and provide a feasible pathway for global green, low-carbon and sustainable development.

Biography

Wangli He is a Professor at East China University of Science and Technology. Her current research interests include networked multi-agent systems, distributed control, optimization and learning, electricity-hydrogen coupled energy systems and autonomous intelligent unmanned systems.
She has published over 150 papers in prestigious academic journals and conferences, including IEEE Transactions, Automatica, and IEEE/CAA Journal of Automatica Sinica. She has led more than 10 major projects, including those under the Science and Technology Innovation 2030 - 'New Generation of Artificial Intelligence' program, the National Natural Science Foundation of China, and the Shanghai Carbon Neutrality Basic Research Special Zone Project. She has been recognized as an Elsevier China Highly Cited Scholar and listed in the Global Top 2% Scientists 'Lifetime Scientific Impact' ranking. Her awards include the First Prize of the Shanghai Natural Science Award, the Chinese Association of Automation Young Scientist Award, the First Prize of the National Teaching Achievement Award (Postgraduate Education), and the Science and Technology Progress Award (Innovative Team) of the Chinese Society for Instrument and Control. She has served as an Associate Editor for the international authoritative journals IEEE Transactions on Neural Networks (2020-2023) and IEEE Transactions on Industrial Informatics (2023-present).

Prof. Zhaohui Jiang Central South University, China

Abstract

Enclosed industrial equipment, such as industrial furnaces, constitutes the core facilities in the raw-material processing sector. Promoting their low-carbon operation requires real-time access to the distributions of internal physical fields and the reaction conditions. However, because such equipment operates in a sealed state, conventional detection methods find it difficult to observe multimodal information such as the three-dimensional morphology of internal materials and the temperature field. To this end, our research team established a low-light imaging framework for confined spaces under localized dynamic glare interference; designed an endoscopic optical system with a large depth of field, a wide field of view, and a large aperture; and developed a starlight-grade high-temperature industrial endoscope and an intelligent sensing system. This system has been successfully applied to the blast-furnace ironmaking process and, for the first time worldwide, enabled the clear and accurate acquisition of multimodal information, including the three-dimensional morphology of the blast-furnace burden surface and temperature field, thereby breaking the long-standing “black-box” state of blast-furnace top monitoring.

Biography

Zhaohui Jiang is a Professor and Doctoral Supervisor at Central South University, where he serves as Director of the Department of Automation Science and Technology. He is a Distinguished Professor under the Ministry of Education’s Changjiang Scholars Program and a Leading Talent in Scientific and Technological Innovation in Hunan Province. His teaching and research primarily focus on intelligent detection and perception, image processing, and pattern recognition. He also serves as a Council Member of the China Society of Image and Graphics (CSIG), Vice Chair of the Applications Committee of the Chinese Association of Automation, and Vice President of the Hunan Association of Automation. He has led 30 national-level and university–enterprise collaborative projects, including a National Major Scientific Research Instrument Development Project, a key project under the Ministry of Industry and Information Technology’s major special initiative on intelligent manufacturing, and an Industrial Internet Innovation and Development Project. He has authored one monograph, published over 100 academic papers, and been granted 72 Chinese invention patents. He has received five Highest-prize awards and First Prizes in provincial- and ministerial-level science and technology awards, as well as honors including the 9th CAA Young Scientist Award and the Award for Individuals at the 13th Invention and Entrepreneurship Awards.

Prof. Jinhai Liu Northeastern University, China

Abstract

Magnetic flux leakage (MFL) in-line inspection is the primary safety assessment method for long-distance oil and gas pipelines. However, the current intelligent analysis of MFL data suffers from low accuracy and poor applicability due to the diverse configurations of inspection tools and complex operating environments. Furthermore, rapid advancements in general artificial intelligence (AI) technologies are difficult to apply directly, as pipeline operating conditions fundamentally differ from the scenarios for which these general AI technologies were designed. Consequently, designing non-destructive testing (NDT) intelligent modeling methods that are both highly accurate and broadly applicable remains a significant technical bottleneck in the digitalization of NDT. To address the intelligent analysis challenges in pipeline in-line inspection, our research group has conducted extensive long-term studies, culminating in a comprehensive knowledge-driven data analysis framework. This framework encompasses intelligent data preprocessing methods, evolutionary defect detection algorithms, and mechanism-integrated pipeline defect assessment techniques. The developed system has been successfully deployed and widely adopted by major industry stakeholders, including CNOOC and the National Pipeline Network.

Biography

Jinhai Liu is a Professor at Northeastern University and serves as the Deputy Director of the Institute of Intelligent Electrical Science and Technology. He is also recognized as a Scientific and Technological Innovation Leader under the "Xing Liao Talent Program." He has long been engaged in research on intelligent pipeline inspection and safe operation. The technologies and systems he has developed are widely applied across 25 provinces in China, as well as in the Middle East and South Africa, serving major clients such as CNOOC, Sinopec, CNPC, and PipeChina.
His research interests include industrial intelligence, special-purpose robotics, and electromagnetic non-destructive testing technologies. He has received over 10 scientific and technological awards, including the Second Prize of the State Scientific and Technological Progress Award. He has published more than 100 papers indexed by SCI and holds over 90 authorized invention patents. He serves as a review expert for the National Natural Science Foundation of China and a panel expert for the Ministry of Science and Technology. He has led over 30 research projects commissioned by national, provincial, municipal, and corporate entities, including key and major projects funded by the National Natural Science Foundation of China, key topics within the National Key R&D Program, major sub-topics of the 863 Program, and significant enterprise research initiatives.

Abstract

With the wave of digital transformation sweeping across the globe, the scale and complexity of enterprise information systems have increased dramatically, and traditional operations and maintenance (O&M) models are facing unprecedented challenges. Against this backdrop, complex system state awareness and intelligent operations and maintenance have emerged as a bridge connecting traditional O&M with the intelligent world of the future. Complex system state awareness and intelligent O&M integrate cutting-edge technologies such as big data, large models, artificial intelligence, machine learning, and visualization, aiming to reshape the connotation of O&M and achieve a more efficient, stable, and secure operational environment. This forum aims to build an open and shared communication platform, bringing together experts and scholars from fields such as large models, artificial intelligence, graphics and imaging, and machine learning, focusing on frontier topics in research directions including complex system state awareness, state monitoring, performance evaluation, lifetime prediction, diagnostic decision-making, self-healing recovery, and collaborative optimization. We look forward to exploring new paradigms of complex system state awareness and intelligent O&M through communication and exchange, providing theoretical and methodological support and feasible references for the innovative development of intelligent manufacturing.

Chair: Prof. Jing Na Kunming University of Science and Technology, China

Biography

Dr Jing Na is currently a Chair Professor in Dynamics and Control, affiliated with the Faculty of Electrical & Mechanical Engineering at Kunming University of Science and Technology (KUST). He received the B.S. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology (BIT), China, in 2004 and 2010, respectively. From January 2011 to December 2012, he was a Monaco/ITER Postdoctoral Fellow with the ITER Organization, France, where he has worked on the modeling and control for cryogenic systems. From January 2015 to December 2016, he was a Marie Curie Fellow with the University of Bristol, U.K, where he has worked on the estimation and control for engine systems and vehicles.
His current research interests focus on the parameter estimation, intelligent control, adaptive optimal control and nonlinear control with application to vehicle systems, servo mechanisms, robotics and energy conversion plants (e.g. engine, fuel cell, wave energy convertors, etc.) On these topics, He has coauthored 3 monographs, 5 book chapters, and published more than 150 peer reviewed journal and conference papers, attracting more than 10000 citations. Dr Na has been awarded the Fok Ying Tung Award (2021), the Hsue-shen Tsien Paper Award (2017), and the highly competitive EU Marie Curie Intra-European Fellowship (2014).
He is currently an Associate Editor of IEEE Transactions on Industrial Engineering, Neurocomputing, Control and Decision. He has also served as the Program Chair of ICMIC 2017, CCC 2024, General Co-Chair of DDCLS 2019, Organizing Committee Chair of CCDC 2021, and the IPC member of many international conferences (e.g., IEEE CASE, IEEE CIS&RAM, IFAC ICONS, etc).

Chair: Prof. Fei Chu China University of Mining and Technology, China

Biography

Fei Chu is a Professor at China University of Mining and Technology. He is a recipient of the National High-Level Young Talent Program, the Jiangsu “Six Talent Peaks” Program, the High-End Talent Program of China University of Mining and Technology, and the China University of Mining and Technology Youth May Fourth Medal. He is a Senior Member of the Chinese Association of Automation and an IEEE Senior Member. He also serves as a member of the Technical Committee on Process Control and the Technical Committee on Fault Diagnosis and Safety of the Chinese Association of Automation, a member of the Technical Committee on Intelligent Simulation Optimization and Scheduling of the China Simulation Federation, a council member of the Jiangsu Association of Automation, and Deputy Director of the Jiangsu Technical Committee on Process Control. In addition, he serves as an Associate Editor of the Journal of Control and Decision and Journal of the Franklin Institute, a member of the Editorial Board of Control and Decision, and a member of the Young Editorial Board of the Journal of China University of Mining and Technology. His main research interests include artificial intelligence-driven intelligent modeling and optimal operational control of complex industrial processes; operating condition monitoring, risk assessment, and fault diagnosis of complex systems and equipment; and broad learning, deep learning, and transfer learning. He has led more than 20 national-, provincial-, ministerial-, and industry-funded projects, published over 100 academic papers in leading journals in artificial intelligence and control, been granted more than 20 invention patents, and developed and deployed more than 10 industrial software systems. His research achievements have received more than 10 awards, including the First Prize of the Science and Technology Progress Award of the Chinese Association of Automation, the First Prize of the Science and Technology Progress Award of the China General Chamber of Commerce, the Second Prize of the Science and Technology Award (Technical Invention) of the China Nonferrous Metals Industry, the 2025 Outstanding Young Flotation Engineer Award at the China Flotation Conference, the First Young Scientist Award of the Jiangsu Association of Automation, the CPCC Zhang Zhongjun Academician Outstanding Paper Award, the Best Paper Award at the International Unmanned Systems Conference, and the First Prize of the National Coal Industry Teaching Achievement Award.

Chair: Prof. Jiande Wu Yunnan University, China

Biography

Jiande Wu received the B.S. and M.S. degrees in Automation and Mechatronic Engineering from Northwestern Polytechnical University, Xi’an, China, in 2001 and 2004, respectively, and the Ph.D. degree in Automation from Zhejiang University, Hangzhou, China, in 2007. He is currently a Professor at Yunnan University and has been recognized as a National High-Level Leading Talent and a Yunling Scholar. He also serves as the Director of the Engineering Technology Research Center for Intelligent Systems and Advanced Control of the Nonferrous Metals Industry of China.
His research interests focus on fault detection and intelligent control of complex industrial processes, as well as industrial big data analysis and modeling. He has presided over more than 20 research projects, including key projects of the National Natural Science Foundation of China (Regional Joint Fund) and major science and technology projects of Yunnan Province. He has published over 70 papers in prestigious journals such as IEEE Transactions and holds more than 40 authorized patents. His research achievements have received several major awards, including the Second Prize of the National Science and Technology Progress Award, the First Prize of the Yunnan Provincial Science and Technology Progress Award, the First Prize of the Yunnan Provincial Natural Science Award, and the First Prize of the Natural Science Award of the Chinese Association of Automation.

Prof. Yaguo Lei Xi’an Jiaotong University, China

Abstract

High-end equipment plays an important role in the fields such as aerospace, energy and power, and transportation. Faults are the potential threats to their safe and reliable operation. Intelligent operation and maintenance is a vital means to ensure the safe operation of equipment and high-quality production. The speaker will first introduce the methodologies and technologies established by his research team in the field of intelligent equipment operation and maintenance. Then, the application scenarios and typical cases of the developed intelligent diagnosis and operation and maintenance systems will be shared. Finally, the latest research work on large models for intelligent operation and maintenance will be reported.

Biography

Yaguo Lei is a Full Professor of the School of Mechanical Engineering at Xi'an Jiaotong University. He had held the research position as an Alexander von Humboldt Fellow at the University of Duisburg-Essen, Germany, and as a Postdoctoral Research Fellow at the University of Alberta, Canada. He is a Fellow of ASME, IET, and ISEAM, as well as a Senior Member of IEEE, CAA, ORSC, and CMES. He serves as a Senior Editor for Mechanical Systems and Signal Processing and an Associate Editor for IEEE Transactions on Industrial Electronics. Additionally, he is an editorial board member of over ten leading journals. His research interests focus on big data-driven intelligent maintenance, intelligent fault diagnostics and prognostics, reliability evaluation and remaining useful life prediction. He has published four monographs and more than 100 peer-reviewed papers. His work has been cited over 32,000 times, with an H-index of 75 according to Google Scholar. His most-cited paper has received over 2,200 citations. His proposed methodologies and techniques have been widely applied in intelligent condition monitoring and diagnostic systems for renewable energy systems and other industrial domains, such as wind turbines, new-energy vehicles, and high-speed trains, etc. Prof. Lei has received the Xplorer Prize from the New Cornerstone Science Foundation. He has been recognized as a Global Highly Cited Researcher by Clarivate Analytics and a Chinese Most Cited Researcher by Elsevier. He is also listed in the Stanford/Elsevier Global Top 2% Scientists and holds the distinction of being ranked 1st in the field of Acoustics.

Prof. Xiao He Tsinghua University, China

Abstract

Embodied intelligent systems are typical complex dynamic systems characterized by multi-subsystem coupling, multi perception–decision closed loops, strong nonlinearity and uncertainty, and interaction with open environments. To address these challenges, a real-time safety enhancement technology framework for embodied intelligent systems has been developed, covering key components including dynamic system state estimation, fault diagnosis, fault-tolerant control, and safety assessment. A novel networked strong-tracking nonlinear state estimation method is proposed; an active diagnosis theory based on auxiliary signal excitation is developed; the influence mechanisms of faults, component performance, and system structure on consensus and safety margins are revealed; and from the perspectives of efficient utilization of data value and online updating of evaluation models, a new real-time safety assessment method for dynamic systems is proposed. These results significantly enhance the operational safety of embodied intelligent systems.

Biography

Xiao He is a tenured professor at the Department of Automation, Tsinghua University. He serves as the Vice Director of the Institute for Embodied Intelligence and Robotics, Tsinghua University, and the Director of the Institute of Control and Decision in the Department of Automation, Tsinghua University. His research interests include state estimation, fault diagnosis, fault-tolerant control, and real-time safety assessment of dynamic systems. He has published over 300 papers in domestic and international journals and conferences. He has led National Natural Science Foundation projects including Young Scientist Fund A, Young Scientist Fund B, and Key Project. In 2021, he received the Young Scientist Award from the Chinese Association for Automation. He is currently the deputy secretary-general of Chinese Association of Automation (CAA), deputy director and secretary-general of the fault diagnosis committee of the CAA, deputy director of the process control committee of the CAA, deputy director of the intelligent control and systems committee of the Chinese Institute of Command and Control. He serves as an editorial board member for several international journals, including IEEE TNNLS, IEEE TASE and Control Engineering Practice. He has received the First Prize of the Jilin Province Science and Technology Progress Award in 2018, the First Prize of the CAA Natural Science Award in 2015 and 2020, the First Prize of the CAA Technical Invention Award in 2022, and the Second Prize of the Beijing Natural Science Award in 2023. Four doctoral students he has supervised (including co-supervised) have won the Outstanding Doctoral Dissertation Award of the CAA in 2018, 2021, 2022, and 2024.

Prof. Xiaosheng Si Rocket Force University of Engineering, China

Abstract

Remaining useful life (RUL) prediction, known as prognosis, is a key technology for achieving health management of randomly degraded equipment. The statistical data-driven approach is a typical method in the field of the RUL prediction, but existing statistical data-driven methods generally adopt a strategy of determining the degradation model form based on historical degradation monitoring data of similar devices and updating model parameters using online monitoring data. Such strategy ignores the problem of mismatch between the model function form and data caused by individual differences and time-varying operating environments, which in turn affects the prognosis accuracy. This report will introduce a new method for predicting the RUL under dynamic calibration of a stochastic degradation model, which achieves simultaneous dynamic calibration of the function form and parameters of the degradation model, helping to improve the accuracy and robustness of prognosis.

Biography

Xiaosheng Si received the B.Eng., M.Eng., and Ph.D. degrees in control science and engineering from the Department of Automation, PLA Rocket Force University of Engineering, Xi’an, China, in 2006, 2009, and 2014, respectively.
He is currently a Professor with the PLA Rocket Force University of Engineering. He has authored or coauthored more than 50 articles in several journals, including European Journal of Operational Research, IEEE Transactions on Industrial Electronics, IEEE Transactions on Reliability, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics—Part A, IEEE Transactions on Automation Science and Engineering Reliability Engineering and System Safety, and Mechanical Systems and Signal Processing. His research interests include evidence theory, expert systems, prognostics and health management, reliability estimation, predictive maintenance, and lifetime estimation. Dr. Si is an Editorial Member of Mechanical Systems and Signal Processing, and ASME/IEEE T Mech. He is an active reviewer of a number of international journals.

Prof. Chen Peng Shanghai University, China

Abstract

Large-scale networked systems (LSNSs) constitute a class of complex interconnected systems that encompass critical infrastructures such as the industrial internet and smart grids. Their applications penetrate deeply into key sectors of the national economy, including industrial production and energy supply. In complex dynamic environments, the development of scalable control theories and methods that meet requirements such as "plug-and-play" holds significant theoretical and practical importance. This report first analyzes the structural characteristics of LSNSs and the primary challenges associated with achieving "plug-and-play" scalable control. It then systematically reviews research approaches based on methods such as Cholesky decomposition, spectral graph decomposition, linearly independent Laplace transforms, small-gain theory, and robust invariant sets, discussing their respective strengths and limitations in application. Finally, the report identifies several promising research directions worthy of further exploration in this field.

Biography

Chen Peng received the Ph.D. degree in control theory and control engineering from the Chinese University of Mining Technology, Xuzhou, China, in 2002, respectively. From November 2004 to January 2005, he was a research Associate with the University of Hong Kong, Hong Kong. From July 2006 to August 2007, he was a Visiting Scholar with the Queensland University of Technology, Brisbane, QLD, Australia. From July 2011 to August 2012, he was a Postdoctoral Research Fellow with Central Queensland University, Rockhampton, QLD, Australia. In 2012, he was appointed as an Eastern Scholar with the Municipal Commission f Education, Shanghai, China, and joined Shanghai University, Shanghai. His current research interests include networked control systems, distributed control systems, smart rid, and intelligent control systems.
Dr. Peng Chen is a Senior Member of IEEE, the former Chair of the IEEE IES Technical Committee on Networked Control Systems and Applications, and the current Chair of the IEEE PES Technical Committee on Smart IoT and Control (China). He currently serves as an Associate Editor for multiple international journals, including IEEE Transactions on Industrial Informatics, Information Sciences, and Transactions of the Institute of Measurement and Control. From 2020 to 2024, he was consecutively recognized as a "Highly Cited Researcher" by Clarivate. He has published four Springer monographs, authored over a hundred papers in IEEE Transactions, and has a Google Scholar h-index of 77.

Prof. Jiande Wu Yunnan University, China

Abstract

The resource-based chemical industry represented by tin chemical production is a key pillar of Yunnan Province’s economy, where process control plays a crucial role in production efficiency, product quality, energy utilization, and operational safety. Considering the complex characteristics of tin chemical processes, including multi-batch operation, strong coupling, multi-source disturbances, and significant dynamic fluctuations, this study develops an intelligent monitoring and optimal control framework for the methyltin reaction process. A mechanism–data fusion driven multi-stage process model is established to capture the dynamic relationships among key process parameters. An online monitoring and performance evaluation method is proposed to identify deviations and trend variations of critical variables, while a feature-correlation-based diagnosis approach is developed to trace root causes of process fluctuations under multi-source disturbances and cross-unit propagation. In addition, a multi-objective optimization control strategy is designed for batch-wide coordination to achieve intelligent regulation of key operating variables and improved operational performance.
Based on the above key technologies, the first intelligent control system for methyltin chemical reactions in China has been developed and deployed on eleven production lines at the methyltin workshop of Yunnan Tin Group. Industrial operation results show that production efficiency per line increases by 37.5%, the production cycle is shortened by 21%, energy consumption is reduced by over 20%, labor productivity increases by 18.2%, and the unit processing cost decreases by 16.2% year-on-year, while process stability and product quality consistency are significantly improved. To date, the system has generated cumulative economic benefits exceeding 2 billion RMB and profits of over 100 million RMB. The results provide a scalable intelligent monitoring and optimal control paradigm for complex tin chemical processes, supporting the high-quality and green development of the tin chemical industry.

Biography

Jiande Wu received the B.S. and M.S. degrees in Automation and Mechatronic Engineering from Northwestern Polytechnical University, Xi’an, China, in 2001 and 2004, respectively, and the Ph.D. degree in Automation from Zhejiang University, Hangzhou, China, in 2007. He is currently a Professor at Yunnan University and has been recognized as a National High-Level Leading Talent and a Yunling Scholar. He also serves as the Director of the Engineering Technology Research Center for Intelligent Systems and Advanced Control of the Nonferrous Metals Industry of China.
His research interests focus on fault detection and intelligent control of complex industrial processes, as well as industrial big data analysis and modeling. He has presided over more than 20 research projects, including key projects of the National Natural Science Foundation of China (Regional Joint Fund) and major science and technology projects of Yunnan Province. He has published over 70 papers in prestigious journals such as IEEE Transactions and holds more than 40 authorized patents. His research achievements have received several major awards, including the Second Prize of the National Science and Technology Progress Award, the First Prize of the Yunnan Provincial Science and Technology Progress Award, the First Prize of the Yunnan Provincial Natural Science Award, and the First Prize of the Natural Science Award of the Chinese Association of Automation.

Prof. Fei Chu China University of Mining and Technology, China

Abstract

The recommendations for China’s 15th Five-Year Plan emphasize the need to upgrade key industries, consolidate and enhance the position and competitiveness of sectors such as mining and metallurgy in the global industrial division of labor, and fully implement the “AI+” initiative to drive transformation in scientific research paradigms through artificial intelligence. Although China’s mineral processing and metallurgical technologies are on par with those of leading countries, there remains considerable room for improvement in the development and application of automation and intelligent control technologies for these processes. In the current context of intelligent manufacturing and artificial intelligence, advancing innovation in industrial intelligence-driven integrated intelligent optimization and control technologies for the safe and efficient operation of mineral processing and metallurgical processes is of great practical significance for improving the automation and intelligence of process operation and control, ensuring production safety, and enhancing the overall economic performance of mining enterprises. It can also lay the theoretical and technical foundation for the practical deployment of large-model technologies in the intelligent operation and management of mineral processing and metallurgical processes. This report presents our team’s recent explorations and progress in industrial intelligence-driven integrated optimization and control technologies for the safe operation of mineral processing and metallurgical processes, together with several practical application results of related intelligent technologies.

Biography

Fei Chu is a Professor at China University of Mining and Technology. He is a recipient of the National High-Level Young Talent Program, the Jiangsu “Six Talent Peaks” Program, the High-End Talent Program of China University of Mining and Technology, and the China University of Mining and Technology Youth May Fourth Medal. He is a Senior Member of the Chinese Association of Automation and an IEEE Senior Member. He also serves as a member of the Technical Committee on Process Control and the Technical Committee on Fault Diagnosis and Safety of the Chinese Association of Automation, a member of the Technical Committee on Intelligent Simulation Optimization and Scheduling of the China Simulation Federation, a council member of the Jiangsu Association of Automation, and Deputy Director of the Jiangsu Technical Committee on Process Control. In addition, he serves as an Associate Editor of the Journal of Control and Decision and Journal of the Franklin Institute, a member of the Editorial Board of Control and Decision, and a member of the Young Editorial Board of the Journal of China University of Mining and Technology. His main research interests include artificial intelligence-driven intelligent modeling and optimal operational control of complex industrial processes; operating condition monitoring, risk assessment, and fault diagnosis of complex systems and equipment; and broad learning, deep learning, and transfer learning. He has led more than 20 national-, provincial-, ministerial-, and industry-funded projects, published over 100 academic papers in leading journals in artificial intelligence and control, been granted more than 20 invention patents, and developed and deployed more than 10 industrial software systems. His research achievements have received more than 10 awards, including the First Prize of the Science and Technology Progress Award of the Chinese Association of Automation, the First Prize of the Science and Technology Progress Award of the China General Chamber of Commerce, the Second Prize of the Science and Technology Award (Technical Invention) of the China Nonferrous Metals Industry, the 2025 Outstanding Young Flotation Engineer Award at the China Flotation Conference, the First Young Scientist Award of the Jiangsu Association of Automation, the CPCC Zhang Zhongjun Academician Outstanding Paper Award, the Best Paper Award at the International Unmanned Systems Conference, and the First Prize of the National Coal Industry Teaching Achievement Award.

Abstract

With the rapid advancement of artificial intelligence technology, engineering education is facing unprecedented opportunities and challenges. This forum, themed "AI-Empowered Engineering Education," brings together experts and scholars from the educational and industrial sectors to explore how AI can reshape the model of engineering talent cultivation. The forum will focus on topics such as AI-driven curriculum reform, the construction of intelligent teaching platforms, the supervision and evaluation of teaching quality, and new pathways for the integration of industry and education. The aim is to propel engineering education to keep pace with the times, cultivate versatile engineering talents with innovative capabilities and practical skills, and inject new momentum into industrial development.

Chair: Prof. Qiuye Sun Shenyang University of Technology, China

Biography

Qiuye Sun , Vice President of Shenyang University of Technology, is a Second-Class Professor and Doctoral Supervisor. He is a Leading Talent in Science and Technology Innovation under the National Ten Thousand Talents Program, a National Model Teacher for Ideological and Political Education in Courses, and enjoys the Special Government Allowance of the State Council as an IET Fellow. He has won over ten important awards including the Second Prize of the National Natural Science Award and the Second Prize of the National Science and Technology Progress Award, as one of the top three contributors, meanwhile holds the positions such as the Chairperson of the IEEE PES China Energy Internet Committee, the Chairperson of the Liaoning Electrical Engineering Society, and the Secretary-General of the Energy Internet Specialized Committee of the Chinese Association of Automation.

Prof. Mou Chen Nanjing University of Aeronautics and Astronautics, China

Abstract

As the "brain" of strategic equipment such as large aircraft and advanced fighters, the automatic control system serves as a core technological pillar in building a strong nation in aerospace. It is evolving toward intelligent control, creating an urgent demand for high-level talents in intelligent flight control who are interdisciplinary, innovative, and application-oriented. Based on research findings from multiple postgraduate education reform projects at the provincial and ministerial level or above, this report elaborates on the constructed three-circle collaborative training model encompassing "knowledge system, training platform, and evaluation method". Through in-depth coordination featuring "guidance, drive, and optimization", innovative postgraduate training in the field of flight control has been achieved.

Biography

Prof. Mou Chen , an IEEE Fellow, IET Fellow and a CAA Fellow, serves as the Dean of the College of Automation Engineering at Nanjing University of Aeronautics and Astronautics. He was the recipient of the National Science Fund for Distinguished Young Scholars in 2018, was selected into the National “Hundred-Thousand-Ten Thousand Talents Project” in 2019, and was included in the “New Century Excellent Talents Support Program” of the Ministry of Education in 2011. Currently, he serves as an editorial board member of several SCI-indexed English journals, such as IEEE Trans. Cybernetic、IEEE/ASME Trans. Mechatronics、IEEE Trans. CS II: Express Briefs, etc., and also serves as an editorial board member of Chinese journals including Science China: Information Sciences, Acta Aeronautica et Astronautica Sinica, Acta Automatica Sinica, Control Theory & Applications, etc. He has successively won the Second Prize of the National Natural Science Award (ranked second), the First Prize of the Jiangsu Provincial Science and Technology Award (ranked first), the First Jiangsu Provincial Outstanding Contribution Award for Young Scientists and Technologists, the 2 First Prize of Provincial and Ministerial Award (ranked first), and 2 Second Prizes of the National Defense Science and Technology Progress Award (ranked first). He has applied for and been authorized more than 50 invention patents. He has published 3 monographs in Chinese and English and has published more than 200 academic papers.

Prof. Shuang Wang XIdian University, China

Abstract

Education digitalization has become a pivotal strategic initiative for countries worldwide in advancing high-quality talent cultivation. Artificial intelligence technology, as a new trend in education digitalization, is profoundly impacting knowledge production and acquisition methods, postgraduate education models, and mentor guidance style, and comprehensively reshaping the postgraduate education ecosystem. Building on a systematic analysis of the challenges and opportunities that AI technology brings to postgraduate education ecosystems, this report takes Xidian University as an example. It details the university's practical experiences and achievements in digital education development through the ‘Liu Xin’ strategy, providing valuable insights for higher education institutions.
I. Development of Postgraduate Education at Xidian University
II. Digitalization Trends and Context in Postgraduate Education
III. Digital Exploration and Practice in Postgraduate Education

Biography

Shuang Wang is the Dean of the Postgraduate School at Xidian University and the Executive Dean of the National College for Excellent Engineers, also a professor and doctoral supervisor, and has been selected as a National young talent. She serves as a council member of the Chinese Association for Artificial Intelligence (CAAI), Deputy Secretary-General of the Education and Training Committee, Chinese Institute of Electronics (CIE), council member of the Postgraduate Education Branch, Chinese Society of Electronics Education (CSEE), executive member of Technical Committee on Computer Applications, China Computer Federation (CCF), committee member of the Technical Committee on Intelligent Geophysics, Chinese Geophysical Society (CGS), committee member of the Technical Committee on Tri-Co Robots, Chinese Association of Automation (CAA), and expert committee of the National Postgraduate Electronic Design Contest. She has presided over and participated in multiple national major projects, with research achievements winning 4 first prizes in provincial and ministerial-level science and technology awards and 1 first prize in Shaanxi Provincial Teaching Achievement Award.

Prof. Qingyu Yang Xi'an Jiaotong University, China

Abstract

The Fourth Industrial Revolution, driven by artificial intelligence as its core engine, is accelerating industrial transformation and restructuring, thereby posing fundamental challenges to engineering education worldwide. In this context, the cultivation of outstanding engineering talent has an increasingly urgent need for deep integration between industry and education. This report elaborates on the transformations and challenges facing engineering education, and introduces Xi’an Jiaotong University’s exploration and practice in cultivating outstanding engineering talent. Specifically, it covers the “1121” new model for deep integration of industry, academia, and research; the “1237” new system for cultivating outstanding engineering talent; new talent cultivation models driven by national strategic demands and by innovation consortia; as well as initiatives in integrated undergraduate–graduate education for outstanding engineers and digitally and intelligently empowered engineering practice teaching.

Biography

Qingyu Yang is a Professor and Doctoral Supervisor at Xi’an Jiaotong University. He currently serves as Executive Dean of the National Cellege for Excellent Engineers, Vice Dean of the Graduate School, and Dean of the School of Automation, and is also Chairman of the Shaanxi Instrument and Control Society. His main research interests include intelligent optimization and decision-making, optimization and security of Cyber-Physical Energy Systems (CPES), industrial intelligence and AI security. He has led more than 30 major national-level research projects and university–industry collaborative innovation projects. He has published over 170 academic papers, been granted or disclosed more than 30 invention patents, and has served as chief editor of two textbooks. As the first recipient, he has received one Second Prize of the Shaanxi Provincial Natural Science Award, two First Prizes of the Science and Technology Award of Shaanxi Higher Education Institutions, and one Second Prize of the Shaanxi Provincial Excellent Textbook Award. He has also been honored with more than 10 distinctions, including the Baosteel Excellent Teacher Award, the K. C. Wong Education Foundation Award, the Teacher Ethics Model Award, and the Teaching Excellence Award. In addition, he has supervised three dissertations recognized as Outstanding Doctoral Dissertations of Shaanxi Province, and serves as the person in charge of a Shaanxi Provincial Model Course on Curriculum Ideology and Politics as well as its teaching team.

Prof. Furao Shen Nanjing University, China

Abstract

This report introduces the undergraduate education and training system for the Artificial Intelligence major at Nanjing University. Aimed at nurturing talents capable of original innovation and solving key technical problems, it elaborates on and analyzes the establishment and implementation of the AI training system in line with the principles of strengthening mathematical and computer foundations, deepening professional knowledge in artificial intelligence, mastering interdisciplinary knowledge, and enhancing practical abilities.

Biography

Furao Shen received the B.Sc. and M.Sc. degrees in mathematics from Nanjing University, Nanjing, China, in 1995 and 1998, respectively, and the Ph.D. degree from Tokyo Institute of Technology, Tokyo, Japan, in 2006. He is currently a Full Professor of School of Artificial Intelligence with Nanjing University. His current research interests include neural computing and robotic intelligence.

Prof. Xianghui Cao Southeast University‌, China

Abstract

Cultivating outstanding engineers is a crucial pathway for implementing the integration of education, science and technology, and talent development. Focusing on the pain points across the entire chain of outstanding engineer cultivation, this report introduces the reform explorations of the National Graduate College of Elite Engineers of Southeast University, in areas such as admission, cultivation, and evaluation. Taking the joint cultivation of outstanding engineers between Southeast University and China Unicom in the field of Industrial Internet security as an example, it reports the progress of university-enterprise collaboration, particularly on exploring and practicing aspects like curriculum, case studies, and engineering training.

Biography

Xianghui Cao , Vice Dean of the National Graduate College of Elite Engineers and professor of the School of Automation, Southeast University. He received the B.S. and Ph.D. degrees from Zhejiang University. From 2012 to 2015, he was a Senior Research Associate with Illinois Institute of Technology, USA. His current research interests focus on networked sensing and secure control. He has received one first-class and two second-class provincial/ministerial awards, as well as the Huawei Spark Award, and has been honored as a Zhongying Young Scholar. He serves as an editorial board member for journals such as Acta Automatica Sinica and IEEE Transactions on Industrial Informatics. He is also the Deputy Director of CAA Youth Academic Committee, and a member of CAA TC on Cyber-Physical Systems Control and Decision.

Prof. Qiuye Sun Shenyang University of Technology, China

Abstract

Responding to the demand for constructing a modern industrial system in Liaoning Province, this report elaborates on the core connotation and practical application logic of the "partial differential" education concept in cultivating outstanding engineering talents, based on the construction experience of the School of Elite Engineering at Shenyang University of Technology, By decomposing training elements, reshaping training processes, and aligning with industrial needs, it addresses the inherent core essence of cultivating outstanding engineers in local universities. Focusing on the four characteristics of local universities—meeting students' development needs, solving the dilemmas of university education, empowering regional development, and fulfilling the mission of engineering—the college has innovatively established an integrated distinctive training system of "4 Parts", "6 Micros" and "7 Points". Relying on artificial intelligence (AI) technology to deeply empower the entire process of postgraduate cultivating, it promotes the refinement, personalization and characteristic improvement of talent cultivation system. The "6 Micros" characteristic training measures further deepens the university-enterprise collaborative education ecosystem and focuses on the three integral main lines of "ideological and political education for soul-casting, systematic thinking, and integrated innovation", which strives to cultivate the six core competencies of elite engineers so as to achieve precise alignment between talent training and the needs of regional industrial development. This model is an innovative exploration of artificial intelligence empowering engineering education, providing a replicable, promotable and referable implementation path for local universities.

Biography

Qiuye Sun , Vice President of Shenyang University of Technology, is a Second-Class Professor and Doctoral Supervisor. He is a Leading Talent in Science and Technology Innovation under the National Ten Thousand Talents Program, a National Model Teacher for Ideological and Political Education in Courses, and enjoys the Special Government Allowance of the State Council as an IET Fellow. He has won over ten important awards including the Second Prize of the National Natural Science Award and the Second Prize of the National Science and Technology Progress Award, as one of the top three contributors, meanwhile holds the positions such as the Chairperson of the IEEE PES China Energy Internet Committee, the Chairperson of the Liaoning Electrical Engineering Society, and the Secretary-General of the Energy Internet Specialized Committee of the Chinese Association of Automation.

Abstract

Embodied intelligence (EI) represents an advanced form of artificial intelligence (AI) and a concrete manifestation of bio-inspired intelligence. As such, it has been established as an independent academic discipline in leading domestic universities. Bionic robots serve as one of its key practical implementations. With continuous breakthroughs in control theory, robotics, and EI technologies, embodied intelligent robots have become a key intersection of artificial intelligence and robotics research, and are positioned to serve as the ultimate platform for AI applications. Research in such a field center on three core components: perceptual systems, motor control, and cognitive decision-making. Embodied intelligent robots address forward-looking technological needs across four key dimensions and demonstrate significant potential for application in smart manufacturing, medical rehabilitation, assistive care for the elderly and disabled, security, and defense, among other sectors. The pursuit of high-level technological self-reliance has further created new imperatives for advancing the theoretical foundations and technological innovation of embodied intelligent robots. This forum is designed to provide an academic platform for experts, scholars, and professionals in robotics, control systems, computer science, artificial intelligence, instrumentation, mechanical engineering, and related fields to exchange ideas and share the latest technological developments. Its aim is to accelerate the innovative development and practical deployment of intelligent robots across a wide range of scenarios.
This forum brings together leading domestic experts and scholars to share the latest breakthroughs in cutting-edge research and technologies on embodied intelligent robots. It seeks to foster in-depth discussions on academic trends, broaden research perspectives, advance the integration of robotics and embodied intelligence, and accelerate the application of educational achievements in robot control and decision-making.

Chair: Prof. Binrui Wang China Jiliang University, China

Biography

Binrui Wang , Doctor, professor, and doctoral supervisor, the vice-president of China Jiliang University, the discipline leader of the Provincial First-Class Discipline in Control Science and Engineering, a recipient of the Zhejiang Province New Century Excellent Talent Award, a state-sponsored overseas returnee, and the principal investigator of a National Key R&D Program of China. His main research areas include bionic robotics, intelligent perception & metrology, and related areas. He has published over 200 high-level academic papers, authored 3 monographies and 2 textbooks, held over 50 authorized invention patents as the first inventor, and developed 7 national standards. He has received the second prize from the Chinese Society of Automation and the second prize from the China Instrument and Control Society. He serves as the vice chairman of the Robotics Professional Committee of the China Association for Standardization, a member of the National Standards Committee Technical Committees TC591 and TC307, the vice secretary-general of the National Civil Aviation Metrology Technical Committee, and member of the Space Metrology Technical Committee.

Prof. Weidong Zhang Shanghai Jiaotong University, China

Abstract

Unmanned marine systems, such as USVs and UAVs, play an increasingly vital role in marine development and application due to their advantages of flexibility, efficiency, and low cost. However, the capabilities of a single unmanned system are limited. USV-UAV collaboration can organically integrate different types of unmanned systems, establishing a three-dimensional collaborative operational system to accomplish complex tasks. This approach holds profound significance for fully leveraging the advantages of unmanned marine systems and advancing marine development and utilization.
This report focuses on the challenging and forward-looking field of USV-UAV collaboration, introducing its application requirements in scenarios such as emergency rescue. Drawing on the engineering development experience of the Artificial Intelligence and Robotics Center (AIRC) at Shanghai Jiao Tong University in this field, the report analyzes key technologies of USV-UAV collaboration, explores technical bottlenecks and challenges faced in this domain, and presents multifaceted technical validations conducted by the AIRC team on USVs, UAVs, and USV-UAV collaborative systems.

Biography

Weidong Zhang received his BS, MS, and PhD degrees from Zhejiang University, China, in 1990, 1993, and 1996, respectively, and then worked as a Postdoctoral Fellow at Shanghai Jiaotong University. He joined Shanghai Jiaotong University in 1998 as an Associate Professor and has been a Full Professor since 1999. From 2003 to 2004 he worked at the University of Stuttgart, Germany, as an Alexander von Humboldt Fellow. From 2007 to 2008 he worked at Princeton University, USA, as a Visiting Professor. From 2013 to 2017 he serviced as Deputy Dean of the Department of Automation, Shanghai Jiaotong University. He is currently Chair Professor of Shanghai Jiaotong University, Director of the Engineering Research Center of Marine Automation, Shanghai Municipal Education Commission, China. His research interests include control theory, machine learning theory, and their applications in industry and robots. He is the author of more than 300 papers and 2 books. His papers have been cited for more than 23k times in Google, and he is recognized as Elsevier Most Cited Researcher and Highly Ranked Scholar-Lifetime in the Specialties of Sensor fusion, Contra[ tlieory by ScholarGPS.

Prof. Lei Ren Jilin University, China

Abstract

Humanoid robot has potential applications in a variety of areas. However, poor locomotor energy efficiency, limited manipulation capability and poor physical human-robot interaction safety significantly hinder its advance and practical application, posing a great challenge in the robotics field. To address this problem, we propose a novel idea of bionic layagrity robotic system, inspired by the human musculoskeletal system. We reveal the fundamental principle of biological layagrity system and associated mechanical intelligences by analysing the effects of material property, morphology and topology of the musculoskeletal system on economical locomotion and versatile hand manipulation. By employing advanced functional materials and state-of-art manufacturing technologies, we finally achieve human-like locomotor system with low energy cost and bionic robotic arm-hand system with dexterous manipulation skills and excellent human-robot interaction safety. This will provide theoretical foundation and enabling design and manufacturing techniques for future advanced humanoid robotic systems.

Biography

Lei Ren researches in the field of biorobotics and bionic healthcare by exploring the fundamental musculoskeletal, neuromuscular and sensorimotor principles underlying human movement, whilst developing bioinspired humanoid robots and healthcare devices, and innovative bionic soft actuation and sensing techniques based on the learnt biological principles. He has been the PI and Co-I of over 40 research projects funded by NSFC, MoST, UK EPSRC, BBSRC etc., and has over 360 peer-reviewed journal papers and has been awarded over 280 patents. His research works have been reported by Nature, Science News, BBC etc. He is the standing vice President of the International Society of Bionic Engineering (ISBE), sits in the Council of Chairs, Biomedical Engineering Society (BMES), and serves as the General Secretary of IFToMM UK. He is the associate editor-in-chief of Journal of Bionic Engineering, the associate editors of Frontiers in Bioengineering and Biotechnology, Journal of Mechanical Engineering Science etc.

Prof. Long Cheng University of Chinese Academy of Sciences, China

Abstract

With the rapid development of robotics, improving the tactile and force sensing capabilities of robots has become a critical issue that urgently needs to be addressed. Current tactile and force sensing technologies are mainly based on resistive, capacitive, electromagnetic, piezoelectric and other principles. However, significant challenges remain in achieving high sensitivity, high flexibility, and stable and reliable tactile and force perception. This talk focuses on the design of tactile and force sensors based on the variable optical path principle, reviews the corresponding research progress in embodied manipulation, and further prospects the future development trends of embodied intelligence.

Biography

Dr. Long Cheng is a Distinguished Professor at the University of Chinese Academy of Sciences. He is an IEEE Fellow, IET Fellow, and CAA Fellow. He serves as an Associate Editor for several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, Science China Information Sciences, and Acta Automatica Sinica. He has been awarded the National Science Fund for Distinguished Young Scholars and the Beijing Science Fund for Distinguished Young Scholars. In 2017, he received the Second Class Prize of the National Natural Science Award.

Prof. Yang Cong South China University of Technology, China

Abstract

Embodied AI is moving robots from structured environments to open, dynamic real-world settings. Autonomous manipulation is key to measuring intelligence and determining adaptability in manufacturing, home service, and healthcare. Its foundation lies in perception and cognition. Despite progress in humanoid robots and embodied foundation models—such as vision-language-action modeling and end-to-end control—robots still struggle with tasks humans find trivial: fragile visual recognition under challenging lighting, occlusion, or deformation; poor generalization to unseen objects/scenes; and limited capability for long-horizon or non-rigid manipulation. This report addresses these challenges and explores new approaches to enhance autonomous manipulation in embodied AI systems.

Biography

Yang Cong is a full professor of Chinese Academy of Sciences. He received the B.S. degree from Northeast University in 2004, and the Ph.D. degree from State Key Laboratory of Robotics, Chinese Academy of Sciences in 2009. He was a Research Fellow of National University of Singapore (NUS) and Nanyang Technological University (NTU) from 2009 to 2011, respectively; and a visiting scholar of University of Rochester. His current research interests include robot vision, robot learning, big data, multimedia and medical image analysis. He won the National Science Fund (NSFC) for both Distinguished Young Scholars and Excellent Young Scholars, the first prize of Natural Science Award of Liaoning Province, the first prize of Natural Science Award of Chinese Association of Automation.  He has published more than 80 papers in the international journals and conferences. He served as the associated editor of IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Journal of Automation, and other well-known journals.

Prof. Ning Sun Nankai University, China

Abstract

With the increasing demands for human-robot interaction, the modeling and intelligent control of pneumatic bionic robots have attracted growing attention from researchers worldwide. Pneumatic artificial muscles feature light weight, high safety, and a large power-to-volume ratio, but they also have inherent shortcomings such as strong nonlinearity, hysteresis, and time-variation, which bring severe challenges to the modeling and precise control of robots actuated by them. Therefore, the realization of accurate modeling and intelligent control of pneumatic bionic robots is of great theoretical and practical significance. In recent years, we have conducted in-depth research on the modeling, planning, and control of pneumatic bionic robots. At the end of this report, we will prospect the future research directions and development trends of pneumatic bionic robots, and further report our research progress on other robotic systems, including underactuated robots, non-ferrous metal ingot finishing robots, and variable-structure robots.

Biography

Ning Sun is a Professor at Nankai University and the Shenzhen Research Institute of Nankai University, as well as a dual-appointed Professor at Shenzhen Loop Area Institute. He is a Young Changjiang Scholar, an Excellent Teacher Award winner of Baosteel, and a recipient of the Outstanding Youth Fund of Tianjin. He has led 2 Key Programs of the National Natural Science Foundation of China (NSFC) and 2 projects of the National Key R&D Program of China. He has authored 3 monographs, published more than 80 papers in IEEE Transactions and Automatica, and holds over 30 granted invention patents. He serves as an Associate Editor for IEEE Transactions on Industrial Electronics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and IEEE Transactions on Intelligent Transportation Systems.

Prof. Tao Liu Zhejiang University, China

Abstract

The active toe joint and Achilles tendon system in mammalian limbs play a vital synergistic role in diverse locomotion patterns. Inspired by this, we present BioLeg, a bio-inspired bipedal platform featuring a Spring-Tendon Energy Recirculation System (STERS) that integrates an active toe with a spring-tendon energy recycling mechanism. We design a multi-contact-stage gait to fully leverage the advantages of the STERS while ensuring ease of control. A multi-joint parallel elastic actuator model is established for the STERS. By integrating this dynamics model into the learning process and providing guidance for the proposed gait, the trained policy enables the robot to achieve robust locomotion. Experimental results show that the STERS reduces energy consumption during walking by 34.06%. Compared to a baseline without the STERS, BioLeg demonstrates stronger robustness in both velocity tracking and impact recovery. Furthermore, we validate the effectiveness of the active toe and spring-tendon system in improving energy efficiency, gait tracking accuracy, and balance for bipedal locomotion.

Biography

Tao Liu received the M. Eng. degrees in mechanical engineering from the Harbin Institute of Technology, Harbin, China, in 2003 and the Doctorate degree in engineering from Kochi University of Technology, Kochi, Japan, in 2006. He has been an Assistant Professor in the Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, Japan, from 2009 to 2013. He is currently a Professor of the State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, China.
Dr Liu is also an inventor of one Japan patent about wearable sensors for gait analysis, which was commercialized. He was a recipient of the Japan Society of Mechanical Engineers Encouragement Prize (2010). His current research interests include wearable sensor systems, rehabilitation robots, biomechanics, and human motion analysis.

Abstract

The process industry is a pillar and foundation of the national economy and a crucial support for my country's sustained economic growth. Its risk early warning and intelligent management and control are of great significance for my country's promotion of new industrialization and the implementation of the manufacturing power strategy. This forum focuses on multi-safety risk management and control, perception modeling, early warning decision-making, development of safety management rule sets and toolsets, and the development of industrial software support platforms in the process industry. It will bring together domestic and international experts to discuss the theories, methods, and key technologies of dynamic risk intelligent management and control in the process industry.

Chair: Prof. Youqing Wang Beijing University of Chemical Technology, China

Biography

Professor Youqing Wang is a professor at Beijing University of Chemical Technology, Dean of the School of Information Science and Technology, a National Distinguished Scientist, and an IET Fellow. He has published over 100 journal articles, led over 20 projects, and serves on the editorial boards of several international journals. He is the first scholar from mainland China to receive both the Best Paper Award from the *Journal of Process Control* and the ADCHEM Young Author Award. He has also received the First Prize of the Natural Science Award from the Chinese Association of Automation, the Second Prize of the Natural Science Award from the Ministry of Education, the Second Prize of the Beijing Natural Science Award, the Second Prize of the Shandong Provincial Natural Science Award, and the Fok Ying Tung Young Teacher Award.

Chair: Prof. Ningyun Lu Nanjing University of Aeronautics and Astronautics, China

Biography

Ningyun Lu is a professor at Nanjing University of Aeronautics and Astronautics, vice dean of the School of Automation, and a recipient of the Jiangsu Provincial High-Level Talent Training Program (“333 Project”). He has presided over more than 30 important projects/topics, including the National Natural Science Foundation of China and the National Key Research and Development Program. He has published 4 monographs and more than 160 papers, and has been granted more than 30 invention patents. He has won numerous awards, including the second prize of Jiangsu Provincial Teaching Achievement Award and the first prize of Jiangsu Provincial Science and Technology Award.

Prof. Wenli Du East China University of Science and Techonoly, China

Abstract

With the scope and capabilities of enterprise digitization expand rapidly, planning and scheduling as the "brain" of enterprise production and operation management, directly affects enterprise efficiency and market competitiveness in decisions such as raw material procurement, equipment processing plans, resource and energy allocation, and product distribution etc. It is the main battlefield for enterprise digital transformation and high-quality development, and an important path to promote smart manufacturing industry. The report focuses on the bottleneck of process production planning decision-making, e.g. accurate representation of product distribution and properties, complex feasibility constraints on production decision-making schemes, prediction of intermediate product prices under supply and demand changes, and lack of coordinated operation among multiple production bases. The following core aspects of planning decision-making system, such as the characterization of the energy-material coupled planning procedure, multi-task multi-cycle planning, value factor identification and differential pricing scheme, and large-scale solving algorithms. The analysis is conducted based on practical engineering application scenarios and their benefits.

Biography

Wenli Du received the Ph.D. degree in control science and engineering from East China University of Science and Technology, Shanghai, China, in 2005. She now serves as Director of National Center of Technology Innovation for Smart Process Manufacturing, Director of Shanghai Frontier Science Research Institute of Industrial Intelligence and Intelligent Systems. She has dedicated to innovative research and industrial applications of intelligent control technologies for petrochemical facilities. Addressing key bottlenecks in resource optimization and dynamic control for large-scale production processes, she pioneered the development of full-process digital twins, smart control, and optimized operation systems for ethylene plants and integrated refining-chemical complexes.
Her contributions have been recognized through numerous honors in China, including the National Science Fund for Distinguished Young Scholars, the Changjiang Scholar Professorship, the New Century Excellent Talent award from the Ministry of Education, and the Shanghai Phospherus Program. She has published over 220 papers and authorized 130 patens. She has five State Science and Technology Progress Awards and 13 first prizes of provincial/ministerial-level Science and Technology Awards.

Prof. Jinliang Ding Northeastern University, China

Abstract

The rapid evolution of Artificial Intelligence and the breakthroughs in Large Models are fundamentally driven by the Scaling Laws, which suggest that increasing data volume and model capacity leads to superior performance. However, this success is predicated on data that is highly homogeneous, structurally consistent, and easily harvested at scale. In industrial application scenarios, data no longer follows a unified paradigm. Data generated by different equipment, processes, and production units possesses systematic differences; it exhibits significant scarcity and complex statistical distributions, as well as variations in feature dimensions and organizational forms. This complexity in data characteristics makes it difficult to achieve effective generalization solely through model scaling. This report focuses on these complex data challenges and emphasizes the decisive role of the data foundation in shaping model capabilities. We propose a research framework centered on Data Augmentation and Representation Learning to enhance the learnability of data distributions and the stability of feature expressions. By reconstructing data distribution relationships and feature organization, we aim to build a robust data framework adapted to complex industrial environments.

Biography

Jinliang Ding is a Professor and Doctoral Supervisor at Northeastern University, China. He serves as the Director of the National Key Laboratory of Comprehensive Automation of Process Industries and the Dean of the School of Future Technology. His research interests include modeling, plant-wide control, and optimization for the complex industrial systems, machine learning, industrial artificial intelligence, and computational intelligence and application.
Professor Ding has authored or co-authored more than 300 refereed journal papers and refereed papers at international conferences. He has also invented or co-invented more than 50 patents. He was a recipient of the Young Scholars Science and Technology Award of China in 2016, the National Science Fund for Distinguished Young Scholars in 2015, the National Technological Invention Award in 2013, and three First Prizes of Science and Technology Awards of the Ministry of Education in 2006, 2012, and 2018, respectively. One of his articles published on Control Engineering Practice was selected for the Best Paper Award from 2011 to 2013. He was an associate editor for IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, and IEEE Transactions on Circuits and Systems II: Express Briefs.

Prof. Yalin Wang Central South University, China

Abstract

The salt lake chemical industry is a crucial processing industry for refining mineral resources from salt lakes. Its products are widely used in manufacturing, agriculture, and other fields, occupying an important position in the national economy. To accelerate the comprehensive empowerment of new productive forces for the high-quality development of the new salt lake chemical industry, break down information barriers among salt lake chemical enterprises, and improve the current situation of disconnected supply chain/marketing chain/service chain, separated production control, and weak talent foundation, this report systematically summarizes the team's exploration and practice in the field of green, low-carbon, and intelligent manufacturing in the salt lake chemical industry. The report elaborates on the research progress and application effectiveness achieved in three aspects: the development model and integrated platform of network collaborative manufacturing in the salt lake chemical industry, green and efficient production intelligent control technology and application system, and network collaborative service support system and third-party service platform. Finally, it looks forward to the promising vision of promoting the salt lake chemical industry towards high-end, intelligent, and green development through technological innovation.

Biography

Yalin Wang is a professor and doctoral supervisor at Central South University. She serves as the dean of the School of Automation, a distinguished professor of the Yangtze River Scholar Program, an expert receiving special government allowance from the State Council, a New Century Excellent Talent in Education selected by the Ministry of Education, a fellow of the Chinese Association of Automation, a "Globally Highly Cited Researcher" by Clarivate Analytics, the leader of a natural science innovation research group in Hunan Province, and the head of a science and technology innovation team in the province. She concurrently holds positions as the vice chairman of the Autonomous Unmanned Systems Specialty Committee of the Chinese Association of Artificial Intelligence, the vice chairman of the Fault Diagnosis and Safety Specialty Committee of the Chinese Association of Automation, and the executive vice president of the Hunan Provincial Association of Automation. She has long been engaged in research in the field of intelligent perception and optimal regulation of industrial processes. She has presided over 8 national key research and development plan projects/topics, key projects of the National Natural Science Foundation of China, and major project topics. She has won 1 second prize of the National Science and Technology Progress Award, 1 second prize of the State Technological Innovation Award, and 6 first prizes of provincial and ministerial science and technology awards.

Prof. Lei Ren Beihang University, China

Abstract

This report will explore the key technological directions at the intersection of “artificial intelligence and new industrialization”. It summarizes the prominent technologies emerging from the integration and innovation of the Industrial Internet and AI 2.0; elaborates on the new definition and implications, system architecture, key technologies, and typical applications of industrial foundation models in the context of the AI 3.0 era driven by foundation models and intelligent agents; meanwhile, it introduces the models, system architecture, and typical applications of industrial embodied intelligence. Furthermore, it proposes the theoretical and technological framework of the “digital genealogy” to support industrial foundation models and world models for embodied intelligence. Finally, it offers a perspective on future development directions.

Biography

Lei Ren is the recipient of the first National Science Fund for Distinguished Young Scholars in the field of industrial internet and serves as the Chief Scientist of the National Key Research and Development Program in the key special project on industrial software. He is a Second-Level Professor at Beihang University, a Distinguished Professor of the Lantang Program, a professor in both the School of Automation Science and Electrical Engineering and the School of Software, and serves as the Director of the Academic Committee of the National Key Laboratory of Intelligent Manufacturing of Complex Products. He was the first to propose the theoretical and technological framework for industrial foundation models in the academic community both domestically and internationally, and established the first national standard system in this field. He has led over 30 national and provincial-level projects, including major national science and technology special projects, National Key Research and Development Programs, and Major Research Plans of the National Natural Science Foundation of China. He has published over 100 papers in prestigious international journals such as IEEE Transactions, with more than 10,000 citations, and has been recognized in the Stanford University of World's Top 2% Scientists for lifetime impact. He has led or participated in the formulation of 26 international and national standards and holds over 80 patents and software copyrights. As the principal investigator, he has received five first-prize provincial and ministerial awards. He serves as a committee member for more than ten domestic and international professional organizations, including IEEE, CCF, CAAI, and CAA. He is Vice Chair of the IoT and Intelligent Systems Committee of the China Simulation Federation, Vice Chair of the Cloud Control and Decision-Making Committee of the Chinese Association for Command and Control, Executive Director of the China Simulation Federation, and serves on the editorial boards of IEEE TNNLS, TMECH, Science China, and other domestic and international journals. He is the Deputy Chair of the Talent Working Group of the China Industrial Internet Industry Alliance and was the first in China to offer the "Industrial Internet" course at the university level. He has served as the chair of numerous IEEE and other academic conferences, both domestically and internationally, and has delivered over 100 invited keynote and plenary presentations.

Prof. Min Liu Hunan University, China

Abstract

As a strategic emerging industry cultivated and developed in China, high-end equipment manufacturing plays a vital role in serving major national demands, leading national economic development, and ensuring national defense security. The development of industrial foundational software for rapid and precise surface defect detection of high-end equipment products represents a major undertaking in the field of engineering quality inspection and holds significant importance for industrial production. However, in complex industrial environments, existing surface defect detection methods face multiple challenges, including complex and variable backgrounds, scarcity of effective samples, weak defect features, and incomplete information from single visual modalities. These challenges make it difficult to achieve high-precision and rapid detection, forming a critical bottleneck that restricts industrial upgrading and quality improvement. Focusing on the common difficulties of surface defect detection in complex industrial scenarios, our team conducts research on industrial defect detection methods based on multimodal collaborative perception, and completes the integrated application of industrial software and system equipment. The related achievements have been applied in fields such as industrial quality inspection and the manufacturing of key national defense equipment. They support the technological transformation, optimization, and upgrading of relevant industries, and promote the advanced manufacturing industry to move towards the mid-to-high end of the global value chain.

Biography

Min Liu is a Second-Tier Professor at Hunan University and serves as the Secretary of the Party Committee of the School of Artificial Intelligence and Robotics. He is a recipient of the National Science Fund for Distinguished Young Scholars, the Young Changjiang Scholar of the Ministry of Education and the Chief Scientist of a National Key Research and Development Program of China. He received the Bachelor's degree from Peking University and the Ph.D. from the University of California, Riverside. He is the Vice Chairman of the Hunan Association of Automation, the Director of the Key Laboratory of Visual Inspection and Control Technology for Advanced Manufacturing, the Council Member of the China Society of Image and Graphics (CSIG), and Deputy Director of the Youth Working Committee of CSIG.

Prof. Youqing Wang Beijing University of Chemical Technology, China

Abstract

Modern chemical engineering systems exhibit high complexity and strong dynamism, where traditional control methods face perceptual blind spots in nonlinear coupling and multimodal heterogeneous data, struggling to meet the high reliability requirements for fault diagnosis and safety control. This presentation closely aligns with the core needs of chemical process safety, stability, and intelligent operation, establishing a comprehensive research framework spanning "knowledge representation," "fault diagnosis," and "collaborative control." Specifically, it includes: 1. Graph-Model-Based Data Knowledge Representation: Investigates the intrinsic structure and multimodal fusion mechanisms of chemical process data (e.g., sensors, logs, process parameters), develops multi-level collaborative learning and noise-enhanced graph learning models to overcome limitations in semantic correlation extraction for high-dimensional heterogeneous data in complex chemical processes. Further, it employs attribute graph structures, bipartite graphs, and fuzzy inference networks to analyze spatiotemporal correlation patterns and dynamic evolution logic in process flows. 2. AI-Driven Chemical Process Fault Diagnosis: Addresses nonlinear and non-Gaussian characteristics by researching statistically enhanced neural correlation analysis methods. For spatiotemporal coupling and multi-mode switching scenarios, it develops spatiotemporal local analysis and adaptive importance coding dictionary learning algorithms. Additionally, it explores semi-supervised learning under small-sample constraints, expert system integration, and distribution alignment techniques to enhance fault diagnosis interpretability. 3. Game-Theoretic Active Safety Control: For network attack environments (e.g., replay attacks), it studies adaptive resilient control schemes based on backstepping methods. It develops a multivariable process self-healing control framework to achieve automatic setpoint compensation and self-recovery under abnormal conditions. By introducing game theory (zero-sum differential games, Nash equilibrium), it resolves optimal fault-tolerant tracking issues in the presence of actuator failures and mismatched disturbances. Further, it investigates data-driven and mechanistic model hybrid-driven control algorithms for parameter optimization and related challenges.

Biography

Youqing Wang is a recipient of the National Science Fund for Distinguished Young Scholars, an IET Fellow, a Fellow of the Chinese Association of Automation, a Professor and Doctoral Supervisor at Beijing University of Chemical Technology, and Dean of the College of Information Science and Technology. He serves on the editorial or guest editorial boards of nine SCI-indexed journals and is a member of three IFAC technical committees. He has won one Natural Science Award each from the Ministry of Education, Beijing Municipality, and Shandong Province. He has authored three books and published over 160 SCI-indexed papers as the first or corresponding author. He holds more than 20 authorized invention patents, and his research achievements have been applied in leading enterprises such as Sinopec and PetroChina. His papers have been cited over 6,000 times in SCI-indexed publications, with citing authors including more than 40 academicians from China and abroad. Sixteen of his papers have been recognized as ESI 0.1% Hot Papers or 1% Highly Cited Papers, and the citing institutions span over 80 countries. He has been featured multiple times in the list of the World's Top 2% Scientists.

Abstract

With the continuous evolution of artificial intelligence technologies, the field of aircraft autonomous navigation and control has entered a new stage of development, garnering significant attention from both academia and industry. Simultaneously, the widespread deployment of modern aerospace technologies and UAV systems urgently demands more advanced autonomous navigation and control solutions. This imposes unprecedented and stringent requirements on theoretical reconstruction and technological innovation. Addressing how to leverage intelligent autonomous flight navigation and control technologies to significantly enhance the intelligence of aircraft, expand their application scopes and operational modes, and bolster aerospace technological progress and national aerospace security capabilities represents a key challenge with immense strategic potential that must be tackled. The Special Forum on "Intelligent Navigation and Flight Control Theory and Applications" aims to establish an open and interactive platform for academic exchange and intellectual engagement for experts and scholars in the fields of aerospace and intelligent control. Its ultimate goal is to accelerate the innovative development and practical implementation of intelligent technologies.
This forum brings together top-tier domestic experts and scholars to share frontier research findings and the latest technological breakthroughs. Participants will engage in in-depth discussions on prospective academic trends, with the aim of effectively broadening research perspectives and actively promoting the industrial application and transformation of advanced navigation and control achievements.

Chair: Prof. Bin Xu Northwestern Polytechnical University, China

Biography

Professor Bin Xu , Vice Dean of the School of Automation at Northwestern Polytechnical University, holds leadership roles including Chair of the Cognitive Computing and Systems Committee of the Chinese Association of Automation, Director of the Shaanxi Provincial General Aviation Systems Engineering Research Center, and Head of the Shaanxi Sanqin Team. His honors include the National Science Fund for Distinguished Young Scholars, Young Scientist Award from the Chinese Society of Aeronautics and Astronautics, and Shaanxi Youth Science and Technology Award. As primary contributor, he has received four major awards including the First Prize of Chinese Association of Automation and Second Prize of Shaanxi Provincial Science and Technology Award.
Research expertise spans navigation, guidance, and control, with leadership of over 30 projects including Key Program of National Natural Science Foundation of China and Civil Aircraft Special Project under the Ministry of Industry and Information Technology. The achievements have been applied to over 10 industrial units, including Aviation Industry Corporation of China and China Aerospace Science and Industry Corporation, and ground testing and flight verification have been conducted.
Editorial roles include Editor-in-Chief of SCI-indexed International Journal of Micro Air Vehicles, and editorial board positions for IEEE Transactions on Systems, Man, and Cybernetics: Systems, Journal of Intelligent & Robotic Systems, and Acta Automatica Sinica, plus Youth Editorial Board membership for Science China: Information Sciences. Recognized as Clarivate Highly Cited Researcher and Elsevier China Highly Cited Scholar. Service includes Chair of Organizing Committee for 2024 Conference on Control and Decision and General Chair for 2024-2025 Conference on Cognitive Computing and Systems.

Prof. Aiguo Song Southeast University, China

Abstract

This report reviews the current development status and challenges faced by humanoid robots, and analyzes their key technologies. We indicate the embodied intelligence, dexterous hand operation, and teleoperation are key technologies for current humanoid robot applications. As bipedal humanoid robots are still unable to meet the requirements of complex multi-task scenarios in the field of power operation and maintenance in terms of environmental adaptability and operational capabilities, multi-legged dual-arm humanoid robots are a feasible solution for power maintenance. This report introduces some progress and applications of our robot research team at Southeast University, especially in the research of quadruped humanoid dual-arm robots for power maintenance operations.

Biography

Aiguo Song Chief professor at Southeast University, a National Outstanding Young Scientist. He graduated from Southeast University in 1996 with a major in Precision Instrumentation and received Ph.D Degree. Currently, he serves as the dean of the Institute of Space Science and Technology at Southeast University, the deputy director of the National Key Laboratory for Digital Medical Engineering, and the director of the Jiangsu Province Key Laboratory for Robot Perception and Control Technology. He has been engaged in research on robot perception and control technology. He has won one second prize of the National Technological Invention Award, three first prizes of the Technological Invention Award from the Ministry of Education, three first prizes of the Jiangsu Provincial Science and Technology Progress Award, and two first prizes of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award. He has published over 400 SCI papers, which have been cited more than 17,000 times, and has authored three IEEE international standards and seven national standards.

Prof. Panfeng Huang Northwestern Polytechnical University, China

Abstract

Spacecraft payloads are the core components of space missions, and their technological sophistication has emerged as a pivotal strategic asset in the realm of civil-military integration and a critical arena in global space competition. New-generation high-value payloads exhibit characteristics such as multi-scale features, variable stiffness, and cross-generational performance. These features pose significant challenges, including dynamic modeling errors, multi-source disturbance suppression, and control instability under extreme operating conditions. This presentation first outlines the ultra-precision and ultra-stability control requirements for complex spacecraft payloads; then categorizes and analyzes the functions of typical payload systems; subsequently focuses on establishing an ultra-precision and ultra-stable control framework, dissects the dynamic behaviors of rigid (e.g., optical clocks), flexible (e.g., antennas), and ultra-flexible (e.g., tethered systems) payloads in detail, and proposes a collaborative innovation approach integrating “modeling–control–actuation”. Finally, the application of these technologies in projects such as the Mengtian experimental module of the Chinese space station and the “Xihe” experimental satellite is presented. These research efforts have overcome key technical bottlenecks in spacecraft payload control under complex environmental conditions, providing critical technical support for China’s pursuit of leadership in space exploration, with promising broad application prospects and significant societal benefits.

Biography

Panfeng Huang is the dean of the School of Astronautics at Northwestern Polytechnical University, a level-2 professor, and Ph.D. supervisor. He is a recipient of the National Science Fund for Distinguished Young Scholars of China and a Leading Talent of “Ten Thousand Plan.” He is also an expert enjoying the Special Government Allowances of the State Council and serves as Chief Scientist of a National Key Research and Development Program project. His research focuses on space robotics, teleoperation, intelligent spacecraft control, human–machine hybrid intelligence, and cooperative control of multi-agent systems. He has published over 170 SCI-indexed journal papers and has received numerous prestigious awards, including the First Prize of the Shaanxi Provincial Natural Science Award, the First Prize of the Shaanxi Provincial Technological Invention Award, the First Prize of the Military Science and Technology Progress Award.He serves on the editorial boards of several leading journals, including IEEE Transactions on Neural Networks and Learning Systems, Robotica, Acta Automatica Sinica, Chinese Journal of Aeronautics (Chinese and English editions), Control Theory & Applications, Robot, among others.

Prof. Deng Zhihong Beijing Institute of Technology, China

Abstract

This report addresses key challenges in complex environments, including high dynamics induced by high-speed flight and high-speed spin of flight vehicles, insufficiency in navigation information acquisition, and uncertainties from random motion disturbances. It presents research outcomes of high-dynamic intelligent navigation technology, enhancing the environmental adaptability and mission execution capability of flight vehicle navigation systems.

Biography

Deng Zhihong , female, is a Distinguished Professor and doctoral supervisor at Beijing Institute of Technology. She concurrently serves as Executive Director of the Chinese Society of Inertial Technology, Deputy Director of the Popular Science Department, and Director of the Youth Working Committee; she is also Deputy Director of the Engineering Research Center of Navigation, Guidance and Control Technology under the Ministry of Education.
Professor Deng has been selected for national-level talent programs. She is mainly engaged in teaching and research in the field of navigation, guidance and control for high dynamic vehicles. Her research achievements have won 4 national-level science and technology awards, and she has published 10 books and textbooks.

Prof. Xiang Yu Beihang University, China

Abstract

Current unmanned aerial vehicles (UAVs) are confined to "ideal environments, deterministic tasks, and preset modes". The autonomy, safety, and intelligence in strong-disturbance environments urgently need to be improved. Addressing the challenges faced by UAVs, such as the difficulty in separating coupled risk factors, the difficulty in navigation and positioning under strong denial countermeasure conditions, the difficulty in precise control with strong aerodynamic drag and dynamic center-of-gravity shift, and the difficulty in safe flight in unstructured spaces, this lecture presents the team's recent research progress from the perspective of bionic intelligence in aspects including risk learning and prediction algorithms, bionic autonomous navigation, bionic dexterous control, and disturbance utilization. The research aims to endow UAVs with capabilities such as "wise brain, sharp eyes, dexterous hands, and robust body" in environments full of disturbances.

Biography

Xiang Yu is current a Distinguished Professor of Beihang University, Recipient of the National Science Fund for Distinguished Young Scholars and the National High-Level Overseas Young Talents Program. His research focuses on bio-inspired autonomous navigation and safety control of unmanned aerial vehicles. He has published over 90 papers, authored one monograph, and holds more than 60 authorized national invention patents. He is the recipient of the Gold Award at the Global Artificial Intelligence Product Application Expo, the First Prize for Scientific and Technological Progress from the China Instrument and Control Society, and the Gold Medals at the International Exhibition of Inventions in Geneva/Nuremberg. He serves as a member of the Expert Group for a major national project, an associate editor for three IEEE Transactions, a member of the IEEE Technical Committee on Aerial Robotics and Unmanned Aerial Vehicles, and the Executive Deputy Secretary-General of the Navigation, Guidance, and Control Technical Committee of the Chinese Association of Automation.

Prof. Yonggang Zhang Harbin Engineering University, China

Abstract

With the increasingly complex application scenarios of unmanned systems, navigation tasks are facing new challenges such as dynamic environments, complex interference, and no satellite navigation signals. The technical framework in the navigation field is gradually evolving towards multi-source and intelligence. This report introduces how intelligent navigation technology can adapt to the environment, perceive the environment, and integrate swarm intelligence in three application scenarios: underwater autonomous navigation, road network assisted navigation, and cluster relative navigation, in order to improve the performance of autonomous navigation for unmanned systems in complex environments.

Biography

Yonggang Zhang is the Dean of the College of Information and Communication Engineering/College of Integrated Circuits, Harbin Engineering University. He is also the deputy Director of Navigation Instrument Engineering Center of the Ministry of Education, and Member of Chinese Society of Inertial Technology. He is the Chief Scientist of the National Key Research and Development Program. His main research areas include navigation technology and information fusion. He has published more than 170 academic papers. He was the recipient of several awards including IEEE Barry Carlton Award, national talent award and Young Scientist Award of the Chinese Society of Automation.

Abstract

The wave of digital intelligence is systematically reconstructing the development paradigm of various industries. As a core field related to the national economy and human livelihood, medical healthcare is undergoing profound changes brought about by subversive technologies represented by artificial intelligence, big data, and cloud computing. The continuous accumulation of massive multimodal data, coupled with breakthroughs in both computing power and algorithms, is paving new pathways to overcome traditional challenges in healthcare, such as experience reliance, resource disparities, and inefficiency. By empowering clinical insight and medical decision-making, digital intelligence has become a crucial engine for advancing the quality and efficiency of healthcare services. This sub-forum aims to build an open and shared communication platform. It brings together experts and scholars across medicine, decision science, computer science, and data science, focusing on frontier issues in medical decision support, such as medical image analysis, clinical assistant diagnosis, smart hospital management, and medical resource allocation. Through in-depth dialogue and ideological collision, it is expected to explore a new paradigm for medical intelligent decision-making, characterized by greater accuracy, efficiency, and reliability, thereby providing intellectual support and methodological enlightenment for the innovative development of medical healthcare.

Chair: Prof. Zeshui Xu Sichuan University, China

Biography

Zeshui Xu received the Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2003. From October 2005 to December 2007, he was a Postdoctoral Researcher with School of Economics and Management, Tsinghua University, China. He was a Distinguished Young Scholar of the National Natural Science Foundation of China and the Chang Jiang Scholar of the Ministry of Education of China. He is currently a Chair Professor with Sichuan University, Chengdu. He has been elected as the member of AE, EASA, and IASCYS, the Distinguished Fellow of IETI, the Fellow of IEEE, IFSA, RSA, IET, ORS, BCS, IAAM, AAIA, AIIA, ACIS and VEBLEO. He is ranked 6th in Artificial Intelligence & Image Processing and 127th in career scientific impact among World’s top 100,000 Scientists in 2025 (released by Elsevier). He was awarded the 9th IETI Annual Scientific Award in 2024, published 23 monographs by Springer and contributed more than 1000 SCI/SSCI articles to professional journals. He is among the world’s top 1% most highly cited researchers with more than 110,000 citations, and his h-index is 162. He is currently the Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Information Sciences, Artificial Intelligence Review, Journal of the Operational Research, Fuzzy Optimization and Decision Making, etc. His current research interests include intelligent decision-making theory and methodology, optimization algorithms, information fusion, and big data analytics.

Prof. Zeshui Xu Sichuan University, China

Abstract

Driven by the rapid evolution of artificial intelligence, medical decision-making is shifting from traditional rule-based expert systems to intelligent paradigms that integrate multi-modal deep learning and large language models. However, real-world medical data is characterized by its unstructured nature, multi-modality, and high uncertainty. Furthermore, challenges such as “black-box” interpretability, modal alignment, and clinical integration complicate precise data analysis and decision modeling. To address these complexities, probabilistic preference theory serves as a mathematical tool for characterizing fuzzy information and fusing individual cognitive differences, providing essential support for linguistic representation and uncertainty reasoning. Therefore, this presentation focuses on the topic of medical intelligent decision-making driven by probabilistic preference, presenting an introduction to the key theories and applications. First, the developmental trajectory of medical intelligent decision-making is outlined. Subsequently, key methods and technologies for probabilistic preference information representation are introduced. Then, the frameworks, application scenarios, and relevant research outcomes are detailed. Finally, current challenges regarding data processing, decision-making processes, and practical applications are systematically summarized. Moreover, a research outlook is offered from both technical and methodological perspectives, aiming to provide forward-looking insights for the advancement of medical intelligent decision-making.

Biography

Zeshui Xu received the Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2003. From October 2005 to December 2007, he was a Postdoctoral Researcher with School of Economics and Management, Tsinghua University, China. He was a Distinguished Young Scholar of the National Natural Science Foundation of China and the Chang Jiang Scholar of the Ministry of Education of China. He is currently a Chair Professor with Sichuan University, Chengdu. He has been elected as the member of AE, EASA, and IASCYS, the Distinguished Fellow of IETI, the Fellow of IEEE, IFSA, RSA, IET, ORS, BCS, IAAM, AAIA, AIIA, ACIS and VEBLEO. He is ranked 6th in Artificial Intelligence & Image Processing and 127th in career scientific impact among World’s top 100,000 Scientists in 2025 (released by Elsevier). He was awarded the 9th IETI Annual Scientific Award in 2024, published 23 monographs by Springer and contributed more than 1000 SCI/SSCI articles to professional journals. He is among the world’s top 1% most highly cited researchers with more than 110,000 citations, and his h-index is 162. He is currently the Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Information Sciences, Artificial Intelligence Review, Journal of the Operational Research, Fuzzy Optimization and Decision Making, etc. His current research interests include intelligent decision-making theory and methodology, optimization algorithms, information fusion, and big data analytics.

Prof. Guohua Wan Shanghai Jiao Tong University, China

Abstract

Regular health check is critical in personal health management, with over 250 million conducted annually in China. Despite its widespread use, designing an efficient health check policy remains a challenge. Existing clinical guidelines often fail to offer personalized recommendations and overlook the influence of disease correlations on joint disease screening.
We address this issue by developing a stochastic modeling framework based on POMDP (Partially Observable Markov Decision Process) to personalize health check decisions. The model decides optimally when and how to select a subset of screening items for an individual, explicitly accounting for correlations between diseases. To estimate the parameters of the decision model, particularly the joint progression of multiple diseases, we introduce a modified Baum-Welch algorithm and apply it to real-world health check data. In the case study, we demonstrate the application of our personalized health check strategy by designing a joint screening strategy for chronic kidney disease (CKD) and diabetes, which is projected to save 628 million CNY annually in China compared to current guidelines and 558 million CNY compared to single-disease analysis. Our findings highlight that the proposed policy offers more efficient and cost-effective recommendations, with significant potential for improving health outcomes and resource allocation.

Biography

Dr. Guohua Wan is currently a Distinguished Professor of Management Science in Antai College of Economics and Management, Shanghai Jiao Tong University. His research interests include operations planning and scheduling, healthcare operations management and business analytics. He has published over sixty papers in management science journals such as Operations Research, Management Science, Mathematics of Operations Research, and medical journals such as Lancet Digital Health, New England Journal of Medicine Artificial Intelligent, and received multiple best paper awards from INFORMS, POMS and CSAMSE. He is also a recipient of 2024 Science and Technology Research Award from the Operations Research Society of China. As a PI, he has received more than RMB 20 million research funds from NSF of China and the industry. He currently serves as a Senior Editor of “Production and Operations Management”, and is the executive Editor of Journal of Management Analytics (an SSCI Q1 journal published by Taylor and Francis).

Prof. Wenhui Zhou South China University of Technology, China

Abstract

Online healthcare platforms face the challenge of surging patient demand against limited medical resources. To improve service efficiency, platforms have introduced pre-scripted messaging features that allow physicians to use standardized responses, though their impact on patient satisfaction remains controversial. Using an instrumental variable approach, this study examines the dual effects of pre-scripted messaging on both patient satisfaction and service efficiency. Our findings reveal two key results: First, surprisingly, pre-scripted messaging significantly enhances patient satisfaction, contrasting sharply with anecdotal evidence of patient complaints. This satisfaction improvement primarily stems from increased patient engagement—pre-scripted messages encourage patients to ask more questions and engage more deeply. Second, we validate the platform's intended efficiency gains, with physicians serving 19.3% more patients while improving resolution rates by 0.6 percentage points. Heterogeneity analysis shows effects are most pronounced for new patient consultations (likely because new consultations generate relatively higher revenue for physicians, creating financial incentives) and among less experienced physicians. These results challenge the conventional assumption that standardization inevitably compromises personalized service, suggesting a paradigm shift from viewing pre-scripted messaging as mere efficiency tools to engagement catalysts that encourage deep patient participation through thoughtful design, achieving dual improvements in operational efficiency and patient experience.

Biography

Wenhui Zhou is Professor and Vice Dean of the School of Business Administration, South China University of Technology. He is a recipient of the National Science Fund for Distinguished Young Scholars, Chief Scientist of the NSFC Innovative Research Group Project, and a Pearl River Scholar Distinguished Professor in Guangdong Province. He serves as Executive Council Member of the Chinese Society of Management Science and Engineering, Vice President of the Reliability Branch of the Operations Research Society of China, and Vice President of the Guangdong Quality Association. He is also an Associate Editor or editorial board member of several leading international and domestic journals.
Professor Zhou’s research interests include service operations management, smart supply chain management, smart health management, hospital operations management, big data analytics, and quality management. He has published extensively in top-tier journals such as Management Science, Production and Operations Management, Decision Sciences, European Journal of Operational Research, Naval Research Logistics, China Economic Review, as well as leading Chinese journals including Systems Engineering – Theory & Practice and Journal of Management Sciences in China. His research has contributed significantly to advancing theory and practice in service operations, digital health management, smart healthcare, and supply chain innovation.

Associate Prof. Yijie Peng Peking University, China

Abstract

Traditional life science experiments are time-consuming and labor-intensive. Digital microfluidic biochips enable high-throughput experimentation, but their programming and control become extremely complex in high-dimensional, highly constrained environments. Existing systems lack an end-to-end solution that directly bridges natural-language experimental intent to low-level hardware control, and conventional algorithms struggle to handle large-scale droplet scheduling problems with strong temporal dependencies and dynamic constraints. Here, we present Alpha-Droplet, a closed-loop experimental system jointly driven by large language models and deep reinforcement learning. The system first parses natural language intent into phased, structured instructions, and then applies deep reinforcement learning to perform large-scale droplet scheduling in a vast state space, while incorporating physical hardware feedback for closed-loop execution. We validated the system in complex biological experiments such as PCR. Experimental results show that our method supports coordinated scheduling in large-scale scenarios involving more than 1,000 droplets, significantly improving both planning success rate and execution efficiency, and enabling fully automated closed-loop operation of life science experiments. This work establishes a new paradigm for automated experimentation on lab-on-chip platforms.

Biography

Yijie Peng , Associate Professor and PhD Supervisor, Guanghua School of Management, Peking University. Executive Director of the Multi-Agent and Social Intelligence Center, Institute for Artificial Intelligence, Peking University; Director of the Multi-Agent and Industrial Intelligence Lab, Peking University Institute of Information Technology. He received his B.S. in Mathematics and Statistics from Wuhan University and his Ph.D. in Management from Fudan University. He was a postdoctoral fellow at the University of Maryland, College Park and an Assistant Professor at George Mason University, USA. His research interests include simulation modeling and optimization, financial engineering and risk management, artificial intelligence, and healthcare. He has led grants including the Excellent Young Scientists Fund, the Original Exploration Program, and the National Science Fund for Distinguished Young Scholars. He has published in leading journals such as *Operations Research*, *INFORMS Journal on Computing*, *IEEE Transactions on Automatic Control*, and top AI conferences. His awards include the INFORMS Outstanding Simulation Publication Award and the Second Prize of the 9th Higher School Scientific Research Outstanding Achievement Award (Ministry of Education). He currently serves as Field Editor for *Journal of Systems Science and Engineering* and *Journal of Systems Management*, Associate Editor for *Asia-Pacific Journal of Operational Research* and *Journal of Systems Science and Information*, Vice President of the Beijing Operations Research Society, Vice Chairman of the AI Technology and Management Application Chapter of the Management Science and Engineering Association, and Director of the Management Science and Engineering Association.

Prof. Jiapeng Liu Xi'an Jiaotong University, China

Abstract

We present a Bayesian interactive preference-elicitation framework for Multiple Criteria Decision Aiding that combines fast variational inference with long-horizon query planning. Additive value models are inferred via a tractable Bayesian approach, enabling closed-form uncertainty quantification. Query selection is cast as a finite-horizon Markov Decision Process, and the interactive policy is approximated with Monte Carlo Tree Search, which plans several steps ahead to maximize cumulative uncertainty reduction, avoiding myopic one-step gains. Two reward instantiations — variance-oriented and entropy-based — define complementary measures of posterior uncertainty. During search, node selection follows an Upper Confidence Bound rule that explicitly balances exploration and exploitation, ensuring efficient expansion of the search tree. Extensive computational studies demonstrate that the proposed approach achieves consistently higher agreement with the ground-truth preference ordering than state-of-the-art methods. Inference remains accurate and stable as bias and inconsistency grow, while maintaining responsive runtimes suitable for real-time interaction. In synthetic planning experiments with fixed interaction budgets, the long-horizon policy yields larger reductions in posterior uncertainty than myopic selectors, confirming the value of look-ahead. Overall, the proposed framework offers more informative queries, faster convergence to a reliable preference model, and consistent performance advantages across problem sizes and data conditions.

Biography

Jiapeng Liu , National Young Talent, Professor, Doctoral Supervisor, Director of the Department of Information Systems and Intelligent Business, and Associate Director of the Research Center for Intelligent Decision-Making and Machine Learning at the School of Management, Xi’an Jiaotong University. His current research interests include intelligent decision-making and machine learning, big data analytics and artificial intelligence management, and enterprise digital transformation. In recent years, he has led and participated in multiple research projects, including those funded by the National Natural Science Foundation of China (Young Scientists Fund, General Program, Key Program, Major Research Plan), the National Key Research and Development Program of China, special projects of the Ministry of Industry and Information Technology, and key projects of the *** Engineering Program of the Central ** Science and Technology Commission. His research findings have been published in leading international journals such as INFORMS Journal on Computing, European Journal of Operational Research, Omega, Decision Support Systems, IEEE TKDE, ACM TKDD, Expert Systems with Applications, Knowledge-based Systems, as well as prominent domestic journals including Systems Engineering — Theory & Practice and Journal of Systems Engineering. He serves as a council member of the Intelligent Decision-Making and Game Theory Branch of the Chinese Society of Optimization, Overall Planning and Economathematics, and a member of the Data Science and Knowledge Systems Engineering Committee of the Systems Engineering Society of China. He has received awards including the Shaanxi Provincial Science and Technology Progress Award, the Shaanxi Provincial Higher Education Institution Science and Technology Award, the Li Huaizu Management Research Award, and the INFORMS Academic Award. He has been selected for the National Young Top-notch Talent Support Program and the Shaanxi Provincial “Sanqin Talents” Young Top-notch Talent Support Program.

Abstract

With the development of science and technology, control of network systems composed of multiple autonomous agents has become a new research hotspot. In the research of networked system cooperative control, the complexity of the agent systems themselves, such as nonlinear unmodeled dynamics, external disturbances, and other uncertainties, as well as the complexity of network communication, such as attacks, information loss, and the ever-changing communication topology, present new challenges to existing control methods in terms of handling uncertainties, convergence speed, and control accuracy. This forum focuses on the high-performance control problems of complex network systems, exchanging new ideas, methods, and results in the broad field of nonlinear control, and fostering the development of new high-performance control theories and technologies.

Chair: Prof. Zhiliang Zhao North University of China , China

Biography

Zhiliang Zhao , Professor, PhD Supervisor, and Dean of the School of Electrical and Control Engineering at North University of China. He is also the Deputy Director of the High-Speed Flying Car Shanxi Provincial Laboratory. He holds several concurrent positions, including member of the Control Theory Professional Committee of the Chinese Association of Automation, Deputy Secretary-General of the ADRC Tecnical Committee of the Chinese Society of Command and Control, and editorial board member of journals such as Journal of Decision and Control and Systems Science and Mathematics. His primary research areas include nonlinear systems and control, ADRC, and finite-time control. He has published over 80 papers, and authored two monographs published by Wiley & Sons and Science Press. He has been the principal investigator for several projects, including those funded by the National Natural Science Foundation of China, the Key Project of the Natural Science Foundation of Shaanxi Province. He has received multiple prestigious awards, including the Second Prize in Natural Science from the Ministry of Education, the Second Prize in Natural Science from the Chinese Association of Automation, the Second Prize in Outstanding Natural Science Academic Papers from Shaanxi Province, and the First Prize in Scientific and Technological Achievements from Shaanxi Universities.

Prof. Xin Xin Southeast University‌, China

Abstract

Underactuated multi-link robots are representative nonlinear systems with complex couplings, and the feasibility of controller design is determined first by whether the system is controllable and observable around an equilibrium point or a representative motion under a given actuation–sensing configuration. This talk presents several results of the author on controllability, observability, and strongly structural controllability/observability for n-link planar robots and related multi-link systems. The emphasis is on characterization conditions for the cases with active link(s) or active joint(s), revealing the decisive effects of the coordinated roles of actuator locations, adjacency structures, and sensing configurations on the control properties of the system. These results can be understood from the perspective of minimal driver-node placement and structural analysis in complex networks, and they also provide a theoretical basis for local feedback stabilization and subsequent nonlinear control design of underactuated robots.

Biography

Xin Xin is a National High-Level Talent, Chief Professor (Level II), and Ph.D. supervisor at Southeast University, China. He currently serves as Director of the Key Laboratory of Measurement and Control of Complex Engineering Systems of the Ministry of Education, and Executive Dean of the Institute of Intelligent Unmanned Systems at Southeast University. He received his B.S. degree from the University of Science and Technology of China in 1987, and was recommended for graduate study at Southeast University in the same year. From 1991 to 1993, supported by the Japanese Government Scholarship, he conducted his doctoral research at Osaka University as a co-advised Ph.D. student between China and Japan. He received the Ph.D. degree in Engineering from Southeast University in 1993 and the Doctor of Engineering degree from Tokyo Institute of Technology in 2000. From 1993 to 1995, he carried out postdoctoral research at Southeast University, and from 1995 to 1996, he served there as an Associate Professor. From 1996 to 1997, he was an Advanced Industrial Technology Researcher with the New Energy and Industrial Technology Development Organization (NEDO), Japan. From 1997 to 2000, he was an Assistant Professor at Tokyo Institute of Technology. From 2000 to 2007, he was an Associate Professor at Okayama Prefectural University, and from 2008 to 2023, he was a Professor there. He also served as Assistant Dean of the Faculty of Computer Science and Systems Engineering, Chair of the Department of Systems Engineering, and Vice Director of the International Exchange Center at Okayama Prefectural University. His research interests include intelligent and nonlinear control theory of robotic systems with experimental validation, bio-inspired robots, and humanoid robots. He has published 260 papers in journals, major international conferences, and related academic venues, including IEEE Transactions on Automatic Control, IEEE Transactions on Robotics, and Automatica, and has authored six books. He received the Excellent Paper Award at the 2nd National Conference on Robotics in 1988 and the Division Paper Award of the Society of Instrument and Control Engineers (SICE) Annual Conference on Control Systems in 2004. He has participated in three major national research projects in Japan and has led six Japanese national scientific research fund projects. He has also led projects including the China Postdoctoral Science Foundation, the National Natural Science Foundation of China for Young Scholars, the General Program of the National Natural Science Foundation of China, the Jiangsu Province Double Innovation Talent Program, and the Jiangsu Provincial Frontier Technology R&D Program. He is currently an associate editor of Automatica, and also serves as a member of the Technical Committee on Large Scale Complex Systems of IFAC, a member of the Control Theory Committee of the Chinese Association of Automation, and a senior member of IEEE. He previously served as an associate editor of IEEE Control Systems Letters, the journal of the Society of Instrument and Control Engineers, and the journal of the Robotics Society of Japan.

Prof. Yiguang Hong Tongji University, China

Abstract

In this talk, we introduce some of our recent works on safety of nonlinear systems.   On one hand, we consider safety verification problem, which is related to our works on safety small gain theorems and inverse barrier functions; on the other hand, we talk about safety design, related to the control for the considered systems where there are conflicts in control tasks, and the control when the systems with safety constraints are studied under cyber attacks

Biography

Prof. Yiguang Hong received his B.S. and M.S. degrees from Dept of Mechanics of Peking University, China, and the Ph.D. degree from the Chinese Academy of Sciences (CAS), China. He is currently a professor and deputy director of Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai.  He was a professor of Academy of Mathematicsand Systems Science, CAS, and served as the director of the Key Lab of Systems and Control. Also, he is a Fellow of IEEE, a Fellow of Chinese Association for Artificial Intelligence, and a Fellow of Chinese Association of Automation. Additionally, he was a member of board of governor of IEEE Control Systems Society (CSS), the chair of IEEE CSS membership and public information committee, and the chair of IEEE CSS chapter activities committee. His current research interests include nonlinear control, multi-agent systems, distributed optimization and game, machine learning, and social networks. He serves as Editor-in-Chief of Control Theory and Technology. He also serves or served as Associate Editors for many journals including the IEEE Transactions on Automatic Control, IEEE Transactions on Control of Network Systems, and IEEE Control Systems Magazine. Moreover, he is a recipient of the Guang Zhaozhi Award at the Chinese Control Conference, Young Author Prize of the IFAC World Congress, Young Scientist Award of CAS, the Youth Award for Science and Technology of China, and the National Natural Science Prize of China.

Prof. Chaoli Wang University of Shanghai for Science and Technology, China

Abstract

Offense and defense are widely regarded as the fundamental combat forms that dominate warfare. They constitute the core contradiction of military operations and permeate throughout the entirety of war. With the development of composite guidance, imaging perception, and multi-source information fusion technology, attackers have significantly enhanced their coordination, anti-bait interference capabilities, and maneuverability, making it difficult for defenders to effectively contain them using traditional passive defense methods. This topic discusses the game problem involving attackers and defenders with non-zero capture radii, coordination between two attackers, and targets being areas, as well as attackers and defenders (referred to as a three-body system). By utilizing geometric methods, the winning conditions and corresponding optimal winning strategies for both players in the game are obtained.

Biography

Chaoli Wang ,a professor and doctoral supervisor, obtained his bachelor's degree in mathematics and master's degree in automatic control from the Department of Mathematics, Lanzhou University in July 1986 and July 1992, respectively. In March 1999, he obtained his PhD in Control Theory and Control Engineering from Beihang University. From July 1986 to June 1989, he served as an assistant professor in the Department of Mathematics at Lanzhou University; from July 1992 to August 1995, he was a lecturer in the Department of Electrical Engineering at Henan University of Science and Technology; from April 1999 to April 2001, he was a postdoctoral fellow and associate researcher at the Shenyang Institute of Automation, Chinese Academy of Sciences; from May 2001 to August 2003, he served as an associate researcher (RA) in the Department of Automation and Computer-Aided Engineering at the Chinese University of Hong Kong; in September 2003, he became a professor at the School of Optoelectronics, University of Shanghai for Science and Technology (December 2004). From 2010 to 2016, he served as the vice dean of the School of Optoelectronic Information and Computer Engineering, and since 2016, he has been the dean of the Department of Control. In recent years, he has published over 200 SCI and EI indexed papers and holds 25 patents. As the principal investigator, he has undertaken 4 general projects of the National Natural Science Foundation of China, more than 20 projects from the State Grid Corporation of China, the National Defense Science and Industry Administration, as well as provincial and ministerial level projects, and has successfully supervised 110 master's and doctoral students. In 2005, he was awarded the Shanghai Shuguang Scholar title for his work on nonholonomic control in visual servoing without calibration. In 2018, he received the first prize of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award. In September 2022, Professor Wang Chaoli was awarded the "Outstanding Contribution Award" at the 12th China Intelligent Systems Conference. Currently, he serves as the vice chairman of the Artificial Intelligence Simulation Technology Professional Committee of the China Simulation Society, a council member of the Chinese Association of Automation, a council member of the Shanghai Artificial Intelligence Society, a member of the Shanghai Control Science and Engineering Discipline Review Group, and an expert reviewer for the National Natural Science Foundation of China.

Prof. Guanghui Wen Southeast University, China

Abstract

This talk focuses on the distributed optimization and game problems in swarm embodied intelligence systems. First, starting from the concepts of "embodiment" and "collectiveness," it elaborates on the system characteristics in terms of physical carriers, environmental interaction, and swarm coordination. For distributed optimization, algorithms tailored to various dynamic models—including second-order, heterogeneous linear, general linear, and Euler–Lagrange systems—are proposed. These encompass motion-characteristic-adapted optimization, output coordination, robust optimization, and aggregation-based optimization methods. Their application in cooperative perception and localization of unmanned surface vehicles (USVs) is also discussed. Next, distributed game problems will be discussed, with emphasis on analyzing the coupling mechanism between game and control in USV systems. An integrated approach is proposed, incorporating fully distributed algorithms under both model-known and model-unknown scenarios. A simulation platform for USV swarm game and confrontation is developed. Finally, future research directions are outlined.

Biography

Guanghui Wen is the Vice Dean and Endowed Chair Professor of the School of Automation at Southeast University. He is an IET Fellow, Deputy Director of the Jiangsu National Center for Applied Mathematics, and Executive Deputy Director of the Jiangsu Provincial Information Mathematics Application Center. His long-term research focuses on distributed control theory and engineering, as well as embodied swarm intelligence theory and technology. He has published over 200 academic papers in journals such as Nature Reviews Electrical Engineering, Research, and various IEEE Transactions, and has authored four academic monographs. His work has received over 20,000 SCI citations. He has won two Best Paper Awards from international academic journals and eight Best Paper Awards from domestic and international conferences. He has led more than 30 research projects, including the National Science Fund for Distinguished Young Scholars, the National Science Fund for Excellent Young Scholars, Key Joint Projects of the National Natural Science Foundation of China, Key R&D Program Projects of the Ministry of Science and Technology, etc. He holds 80 national invention patent applications, with 62 granted, and one international PCT patent (a U.S. invention patent). He serves as an editorial board member for several prestigious journals, including IEEE/ASME Trans. Mechatronics, IEEE Trans. Control of Network Systems, IEEE Trans. Industrial Informatics, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. Intelligent Vehicles, IEEE J. Emerging and Selected Topics in Industrial Electronics, IEEE Trans. Systems, Man, and Cybernetics: Systems, and Asian Journal of Control. He has received the Outstanding Editor Award from both IEEE Trans. Industrial Informatics and Asian Journal of Control. His academic honors include the 18th China Youth Science and Technology Award, two first-class Science and Technology Awards (ranking 1st) and one first-class Technological Invention Award (ranking 1st) from national first-level societies, a Gold Medal at the Geneva International Exhibition of Inventions (ranking 1st), the ARC DECRA Fellow, the Asia-Pacific Neural Network Society Young Researcher Award (independent), the Young Scientist Award (independent) and Innovation Award First Prize (independent) from the Chinese Institute of Command and Control.

Prof. Housheng Su Huazhong University of Science and Technology, China

Abstract

This report first introduces the modeling and controllability analysis of cross domain unmanned systems (UAVs and USVs) collaborative networks, focusing on the characteristics of multi-time scales and dispersion of cross domain unmanned systems, and then provides research progress in coordinated control and distributed observation.

Biography

Housheng Su is currently a Full Professor with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. His research interests lie in the areas of multiagent coordination control theory and its applications to autonomous robotics and mobile sensor networks. Dr. Su received the National Science Fund for Distinguished Young Scholars of China in 2024. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics: Systems and the IET Control Theory and Applications.

Abstract

In recent years, China's rail transit construction has made significant achievements and has become an important engine for economic and social development. After more than a decade of technological accumulation and independent research and development, rail transit equipment has become a beautiful business card of Chinese manufacturing. Building a digital, networked, and intelligent innovation system for rail transit equipment is of great significance for promoting China's technological self-improvement and sustained leadership. The digital twin technology that has emerged in recent years continues to mature and expand its application areas. Digital twin has become a core enabling technology for further enhancing the competitiveness of transportation equipment through the bidirectional mapping between information space and the physical world. In this context, key technologies such as data-driven equipment fault prediction, immersive interactive technology supporting transportation equipment operation and maintenance, and multimodal data analysis and knowledge mining for equipment design and maintenance have attracted widespread attention and are currently urgent problems to be solved.

Chair: Prof. Weijiao Zhang China Academy of Railway Sciences, China

Biography

Weijiao Zhang has long been engaged in scientific research, technological breakthroughs, and system integration in multiple fields, including the information management of electric multiple units (EMU) operation and maintenance, railway operation safety monitoring, intelligent operation and maintenance of complex technical equipment, as well as the Internet of Things and industrial internet application technologies. She has led or primarily participated in over 30 major scientific research projects initiated by the Ministry of Science and Technology and China State Railway Group, is responsible for the centralized management of railway information standardization under China State Railway Group. She has received numerous awards, including the First Prize of Science and Technology from the China Railway Society, the Mao Yisheng Award for Young Scientific and Technological Innovation from the China Academy of Railway Sciences, and the title of Outstanding Young Scientific and Technological Talents in Railways. Over 40 academic papers were published.

Chair: Prof. Shiji Song Tsinghua University, China

Biography

Professor Song is engaged in long-term research on machine learning theory, methods and applications, optimization and scheduling of complex production and manufacturing processes, intelligent control of underwater robots, etc., leading the National Natural Science Foundation of China's major scientific instrument development project "Key Technology Research and Prototype Development of Deep Sea Controllable Visual Sampler". The developed equipment breaks through the functional limitations of China's deep-sea TV grab and deep-sea camera towing system, achieving the organic integration of precise detection and fine sampling. Published more than 120 papers in the IEEE Transactions series of journals, won the second prize of the Ministry of Education's Natural Science Award, the first prize of Jiangsu Province's Natural Science Award, and the second prize of Heilongjiang Province's Science and Technology Natural Class. Served as the vice chairman of the Unmanned Systems Professional Committee of the Chinese Command and Control Society, and published in journals such as "IEEE Transactions on Systems, Man, and Cybernetics: Systems" and "Journal of Automation". Editorial board.

Prof. Shiji Song Tsinghua University, China

Professor Song is engaged in long-term research on machine learning theory, methods and applications, optimization and scheduling of complex production and manufacturing processes, intelligent control of underwater robots, etc., leading the National Natural Science Foundation of China's major scientific instrument development project "Key Technology Research and Prototype Development of Deep Sea Controllable Visual Sampler". The developed equipment breaks through the functional limitations of China's deep-sea TV grab and deep-sea camera towing system, achieving the organic integration of precise detection and fine sampling. Published more than 120 papers in the IEEE Transactions series of journals, won the second prize of the Ministry of Education's Natural Science Award, the first prize of Jiangsu Province's Natural Science Award, and the second prize of Heilongjiang Province's Science and Technology Natural Class. Served as the vice chairman of the Unmanned Systems Professional Committee of the Chinese Command and Control Society, and published in journals such as "IEEE Transactions on Systems, Man, and Cybernetics: Systems" and "Journal of Automation". Editorial board.

Prof. Chi Harold Liu Beijing Institute of Technology, China

Abstract

This report is based on the National Key Research and Development Program of China under the "Industrial Software" key special project. In response to the three challenges faced by the Industrial Internet in discrete manufacturing industries—diverse and complex resources, poor data interoperability, and inefficient business collaboration—this study focuses on the development of core components for an Industrial Internet Operating System for discrete industries, comprising both cloud-side and edge-side components. Specifically, this research involves the construction of an intelligent edge gateway for the Industrial Internet, the development of a big data lake along with cloud-edge collaborative industrial big data knowledge transfer techniques, and a dual-scenario-driven industrial engine based on digital twins and multi-task scheduling. These components provide common technical support for Industrial Internet platforms in discrete manufacturing industries. The developed system is ultimately validated through four application scenarios: large-scale collaborative manufacturing and small-batch personalized customization in electronics manufacturing, as well as product quality inspection and equipment status monitoring in textile manufacturing.

Biography

Chi Harold Liu is Vice Dean of the School of Computer Science and Technology at Beijing Institute of Technology, and IEEE Fellow, Currently serves as Director of the Beijing Key Laboratory of Intelligent Information Technology, Fellow of the Chinese Institute of Electronics, Fellow of the Institution of Engineering and Technology (IET), Fellow of the British Computer Society, member of the Expert Advisory Group for the National Information Industry's 14th Five-Year Plan, member of the Technical Committee of the National Information Technology Standardization Committee, Director of the Beijing Experts Association, Director of the Chinese Institute of Electronics, and Distinguished Member of the China Computer Federation. He is an Editorial Board Member of IEEE Transactions on Mobile Computing. He has published over 100 CCF-A papers and 7 ESI highly cited papers, holds more than 60 authorized invention patents (domestic and international), and has registered over 70 software copyrights. He has been named to the World's Top 2% Scientists List (Lifetime Scientific Impact and Annual Scientific Impact; 2024-2026) and Elsevier's Highly Cited Chinese Researchers (2024).

Prof. Zhe Wei Shenyang University of Technology, China

Abstract

The intelligent production line for major national equipment, represented by China’s standard electric multiple unit (EMU) “Fuxing,” imposes higher demands on edge computing technologies to accomplish data collection, uploading, storage, and analysis of equipment-level data under limited hardware resources during the manufacturing and assembly of high-speed railway equipment. The intelligent production line for China’s standard EMU needs to integrate a wider range of data, achieve faster data analysis, optimize more agile manufacturing processes, and improve the production efficiency, product quality, and service responsiveness of manufacturing enterprises. Starting from the issues of edge-side data correlation mining and decoupling, reliable transmission control, and dynamic resource allocation, a self-organizing ecological mechanism for multi-node cascaded networks in intelligent production lines is constructed to address the challenges of edge-side data acquisition, translation, and fusion. A digital twin model based on real-time data and process knowledge is established to realize a closed-loop adaptive decision-making framework for edge-side intelligent perception and optimized regulation, enabling edge-side data collection, analysis, and processing, as well as real-time in-depth perception of equipment operation. A semantic description and information model for edge-side devices is constructed, and a dynamic demand workflow composition mapping method for intelligent production lines is developed to achieve highly available interconnection, computation, and transmission control. A microservice-based modeling and encapsulation method for information resources is established, along with model data feature matching and active resource push mechanisms to address edge-side information resource sharing and collaboration. The key technologies have been demonstrated in typical applications in the manufacturing of discrete and process-oriented core equipment products for standard EMUs.

Biography

Zhe Wei is Vice Chair of the Fourth Industrial Design Professional Teaching Committee of the China Machinery Industry Education Association, Member of the Computer-Aided Industrial Design Committee of the China Graphics Society, Senior Member of the China Mechanical Engineering Society, Executive Dean of the National Industrial Design Center/National Industrial Design Research Institute, Director of the Liaoning Provincial Key Laboratory of Intelligent Manufacturing and Industrial Robotics, and Secretary-General of the Liaoning Provincial Industrial Design Association. He has led two projects under the National Natural Science Foundation of China, led or participated in five major projects under the Ministry of Science and Technology and the Ministry of Industry and Information Technology, and led over ten various scientific research projects in Liaoning Province and elsewhere. He has published more than 40 academic papers and holds over ten authorized patents. He has been awarded the May 1st Labor Medal of Tiexi District, Shenyang City.

Prof. Hui Liu Central South University, China

Abstract

This report first focuses on the third stage of digital twin development, i.e., the predictive digital twin, and analyzes its characteristics, key elements, trends, and significant driving role in the intersection of rail transit and robotics. Second, it elaborates on the "digital-sensing-integration-control" key technologies of predictive digital twin platforms for rail transit, based on the analysis of fundamental robotic technologies such as sensing, navigation/localization, mapping, motion, hand-eye coordination, and human-robot interaction for general industrial robots. Finally, considering the current state of development both domestically and internationally, it compares the development stages of predictive digital twins and rail transit robotics in China, the United Kingdom, and Germany, and discusses future trends with practical case studies.

Biography

Hui Liu is Outstanding Young Scholar of the Chinese Academy of Engineering's Frontiers of Engineering,Young Scientific and Technological Talent of the Ministry of Transport, and recipient of the China-Europe Talent Program jointly sponsored by the National Natural Science Foundation of China and the European Commission. He serves as the Vice Dean of the School of Traffic and Transportation Engineering at Central South University. His research primarily focuses on the interdisciplinary study of artificial intelligence, robotics, big data, and cutting-edge equipment. He has been honored with the Science and Technology Award from the China Intelligent Transportation Systems Association, the Springer Nature "China New Development Award," the Second Prize of Natural Science Award from the Ministry of Education, and the Second Prize of Technological Invention Award from the Ministry of Education.