报告简介：

Driven by the rapid rise of autonomous technologies, the intersection of control engineering and artificial intelligence has emerged as a vital frontier. This presentation bridges these two domains by highlighting the recent research trajectory of the speaker’s laboratory, illustrating how robust control principles can be harmonized with AI to solve contemporary engineering problems. To ensure broad accessibility, the discussion focuses on core concepts and practical implications rather than dense mathematical derivations.

The talk is structured around three primary themes. First, we explore where control meets intelligence in Cyber-Physical Systems (CPS), specifically looking at how to ensure robustness and stability in Vehicular Systems. Second, we examine the integration of Reinforcement Learning (RL) as a powerful learning algorithm for optimizing control performance in dynamic environments. Finally, we introduce a suite of specialized AI methodologies developed by our team—including MEPC for precision signal processing, MPDANET for effective domain adaptation, and CGLS for robust regularization in supervised learning. Ultimately, this talk demonstrates how the synergy between theoretical control and AI-driven innovation provides a foundation for the secure and reliable autonomous systems of the future.

报告人简介：

Park Ju H. received the Ph.D. degree in electronics and electrical engineering from Pohang University of Science and Technology (POSTECH), Republic of Korea, in 1997. He was a Research Associate with the Engineering Research Center, POSTECH, from 1997 to 2000. He joined the Faculty Member of Yeungnam University, where he currently holds the prestigious title of Chunma Chair Professor. A prolific scholar, he has co-authored five influential monographs. His scholarly impact is reflected in over 58000 citations and an H-index of 116. His research interests include nonlinear dynamics, intelligent systems, and artificial intelligence. Prof. Park is a fellow of Korean Academy of Science and Technology (KAST). He is recognized by Clarivate as a Highly Cited Researcher for eight consecutive years. He achieved the rare distinction of being listed in three separate fields of engineering, computer science, and mathematics from 2019 to 2022. He serves as the Receiving Editor for Nonlinear Dynamics (Springer-Nature) and also holds or has held editorial positions with several prestigious journals, including Artificial Intelligence Science and Engineering, The Journal of the Franklin Institute, Applied Mathematics and Computation, IET Control Theory and Applications, IEEE Transactions on Consumer Electronics, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Cybernetics.