Prof. Youxi Wu Hebei University of Technology, China

Biography: 

Youxi Wu received the Ph.D. degree in Theory and New Technology of Electrical Engineering from the Hebei University of Technology, Tianjin, China. He is currently the PhD Supervisor and a Professor with the Hebei University of Technology. He has published more than 30 research papers in some journals, such as IEEE TKDE, IEEE TCYB, ACM TKDD, ACM TMIS, SCIS, INS, JCST, KBS, ESWA, JIS, and APIN. He is a Distinguished Member of CCF and a senior member of IEEE. His current research interests include data mining and machine learning.

Title: Repetitive Sequential Pattern Mining and Its Application in Sequence Feature Extraction

Abstract:Traditional sequential pattern mining methods determine whether a pattern occurs in a sequence while ignoring its frequency of occurrence, making it difficult to effectively assess the importance of the pattern in the sequence. In contrast, repetitive sequential pattern mining calculates the frequency of a pattern's occurrence in the sequence and uses this frequency information to characterize the pattern, thereby more accurately reflecting the intrinsic structure of the sequence. Based on such pattern mining methods, efficient sequence feature extraction can be achieved. Further integration with classification or clustering models can achieve sequence data classification or clustering tasks, not only improving the performance of classification or clustering models but also enhancing their interpretability.

Prof. Shaoping Bai Aalborg University, Denmark

Biography: Shaoping Bai is a full professor at Department of Materials and Production, Aalborg University  (AAU), Denmark. His research interests include wearable sensors, medical and assistive robots, and exoskeletons. Prof. Bai has been the principal coordinators of several national and European research projects. He is a recipient of IEEE  CIS-RAM 2017 Best Paper Award, IFToMM MEDER 2018 Best Application Paper Award and WearRAcon2018 Grand Prize of Innovation Challenges. Prof. Bai was an associate editor of ASME J. of Mechanisms and Robotics, IEEE Robotics and Automation Letters, and is an associator editor of Robotica. He is the founder of BioX ApS, an AAU spin-off on wearable technologies. He is an elected member of IFToMM Executive Council and serves as a deputy chair of IFToMM Denmark.

Title: Development of wearable technologies for human-robot interaction research and applications

Abstract: Wearable technologies, including wearable sensors and exoskeletons, are being advanced rapidly for human-robot interaction research and applications in many areas. This talk will provide a brief overview of wearable technology development at the Exoskeleton Lab, AAU, addressing research challenges in assistive exoskeletons and wearable sensors.  Fundamental research issues including innovative mechanism design, physical human-exoskeleton interaction, sensing and control, and performance assessment will be covered. Novel designs and sensing methods will be presented. Systems of  upper-body and lower-body exoskeletons developed for rehabilitation and workplace assistance will be outlined. In addition, innovations of wearable sensors for general human-robot interfacing are introduced.

Prof. Hui Zhang Tianjin University of Science and Technology, China

Biography: 

Zhang Hui is currently a Professor in the College of Electronic Information and Automation at Tianjin University of Science and Technology. She received her B.Sc. degree in Automation from the University of Science and Technology of China and her Ph.D. degree in Circuits and Systems from the Shanghai Institute of Technical Physics, Chinese Academy of Sciences. Her primary research interests focus on biometric recognition and computer vision. She is dedicated to the continuous exploration and innovation of biometric recognition technology and has made significant contributions to enhancing system security within this field. In response to the increasingly severe issue of presentation attacks, she has developed a series of biometric liveness detection algorithms. These algorithms are capable of effectively distinguishing between authentic human biometrics and various forms of counterfeits, such as photographs, videos, or forged models, thereby greatly improving the reliability and security of biometric authentication systems. Based on these research achievements, Professor Zhang’s team has developed several high-security biometric recognition terminal devices and modules.

Title: Recent Advances in Iris Liveness Detection

Abstract: As biometric application systems become increasingly widespread, the importance of liveness detection has garnered significant attention in the field of security and authentication. Liveness detection aims to ensure that the biometric data being captured is from a real, live person rather than from counterfeit materials or presentation attacks. The report not only highlights the recent advancements in iris liveness detection but also delves into the innovative work conducted by our team.

Prof. Mouquan Shen Nanjing Tech University, China

Biography: 

Mouquan Shen is the Director of the Professor Committee and a Doctoral Supervisor at the College of Electrical Engineering and Control Science, Nanjing Tech University. He completed his postdoctoral research at Southeast University and has been a visiting scholar at several overseas institutions, including The University of Hong Kong, Yeungnam University (South Korea), and the University of Adelaide (Australia). He has been recognized as a high-level talent in the Jiangsu Provincial "Six Talent Peaks" Program and was listed among the Top 2% of Scientists Worldwide in 2024 and 2025. He has led over 10 provincial and ministerial-level projects, including those funded by the National Natural Science Foundation of China. In recent years, he has published more than 100 papers in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics, and IEEE Transactions on Systems, Man, and Cybernetics: Systems, achieving an H-index of 31. He has served as the chair of three international conferences and has been invited to deliver keynote speeches at multiple international conferences. Currently, he is an Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs and has served as a reviewer for over 90 domestic and international journals, including IEEE TAC and Automatica. Additionally, he acts as a communication reviewer for the National Natural Science Foundation of China, the National Key R&D Program of China, and various provincial and municipal science and technology projects. 

Title: Event-Triggered Estimation and Control for Intelligent Power Systems

Abstract: Over the past decades, power systems have gradually evolved toward intelligent operation, making networked control more critical than ever. However, due to constraints such as limited network resources and operating conditions, networked control systems usually face various challenges, including data packet losses, communication delays, signal quantization, actuator saturation, and limited transmission capacity. As a result, well-designed event-triggered mechanisms have become effective tools for improving both resource utilization and system performance. This report focuses on event-triggered estimation and control in power systems. First, the research background and representative existing results on signal transmission issues in power systems are reviewed. Then, several recent advances in event-triggered estimation and control are introduced, including threshold-dependent event-triggered schemes, dynamic event-triggered schemes based on instantaneous and average triggering errors, and cost-guaranteed dynamic event-triggered schemes. Finally, some future research topics in power systems are discussed.