Choh-Ming Li Professor of Mechanical and Automation Engineering
Director, Institute of Intelligent Design and Manufacturing, the Chinese University of Hong Kong
Biography: Wei-Hsin Liao received his Ph.D. in Mechanical Engineering from The Pennsylvania State University, University Park, USA. Since August 1997, Dr. Liao has been with The Chinese University of Hong Kong, where he is Choh-Ming Li Professor of Mechanical and Automation Engineering. His research has led to publications of over 400 technical papers in international journals and conference proceedings, 27 patents. He was the Conference Chair for the 20th International Conference on Adaptive Structures and Technologies in 2009; the Active and Passive Smart Structures and Integrated Systems, SPIE Smart Structures/NDE in 2014 and 2015. He received the T A Stewart-Dyer/F H Trevithick Prize 2005, the ASME 2017 Best Paper Award in Mechanics and Material Systems, the ASME 2021 Energy Harvesting Best Paper Award, and the ASME 2023 Best Paper Award in Structural Dynamics and Control. He is the recipient of the 2020 ASME Adaptive Structures and Material Systems Award and the 2018 SPIE Smart Structures and Materials Lifetime Achievement Award. He is also the recipient of 2023 ASME Leonardo Da Vinci Award for the eminent achievement in the design and invention. Dr. Liao currently serves as an Associate Editor for Journal of Intelligent Material Systems and Structures, and on the Executive Editorial Board of Smart Materials and Structures. Dr. Liao is a Fellow of ASME, HKIE, and IOP.
Speech Title: Control of Robotic Exoskeletons for Motion Assistance
Abstract: Exoskeletons are promising devices for motion assistance of mobility impaired patients and motor ability augmentation of healthy people. Considering the interactive action between exoskeletons and human body, a safe and comfortable human-exoskeleton interaction is essential to achieve effective exoskeleton operations for human motion assistance. We developed a novel cable-driven series elastic actuation (CSEA) system to realize a flexible and portable back-support exoskeleton design with safe, efficient, and sufficient assistive torque output capability. Meanwhile, this mechanism enables the CSEA system to integrate series elastic actuator (SEA) with cable transmission and operates with multiple statuses to leverage SEA advantages and to overcome its torque output limitation. A unified torque controller is designed for stable, continuous, and accurate torque control of the CSEA system despite its discontinuous dynamics during operation status transition. The efficacy of the closed-loop CSEA system to enable an ergonomic and efficient back-support exoskeleton actuation with the capability of accurately delivering desired level of assistance is verified via bench tests and human tests. Results verified that the CSEA system actuated exoskeleton can effectively reduce activity of relevant muscles during trunk flexion and extension motions compared to no exoskeleton case, validating successful application of the CSEA system on the exoskeleton for an effective back support effect. In this talk, the developed devices/systems and key results will be presented.
Shibaura Institute of Technology, Tokyo, Japan
Biography: Chinthaka Premachandra was born in Sri Lanka. He received the B.Sc. and M.Sc. degrees from Mie University, Tsu, Japan, in 2006 and 2008, respectively, and the Ph.D. degree from Nagoya University, Nagoya, Japan, in 2011. From 2012 to 2015, he was an Assistant Professor with the Department of Electrical Engineering, Faculty of Engineering, Tokyo University of Science, Tokyo, Japan. From 2016 to 2017, he was an Assistant Professor with the Department of Electronic Engineering, School of Engineering, Shibaura Institute of Technology, Tokyo, where he was an Associate Professor, from 2018 to 2022. In 2022, he was promoted to a Professor with the Department of Advance Electronic Engineering, School of Engineering and Graduate School of Engineering, Shibaura Institute of Technology, where he is currently the Director of the Image Processing and Robotic Laboratory. His research interests include AI, UAV, image processing, audio processing, intelligent transport systems (ITS), and mobile robotics.
He is a member of IEEE, IEICE, Japan; SICE, Japan; RSJ, Japan; and SOFT, Japan. He has received many awards, including the IEEE SENSORS LETTERS Best Paper Award from the IEEE Sensors Council in 2022 and the IEEE Japan Medal from the IEEE Tokyo Section in 2022. He also received the FIT Best Paper Award and the FIT Young Researchers Award from IEICE and IPSJ, Japan, in 2009 and 2010, respectively. He has served as a steering committee member and an editor for many international conferences and journals. He is the Founding Chair of the International Conference on Image Processing and Robotics (ICIPRoB), which is technically co-sponsored by IEEE. He is currently serving as an Associate Editor for IEEE Robotics and Automation Letters (R-AL) and IEICE Transactions on Information and Systems.
Nanyang Technological University, Singapore
Biography: Dr. Guoqiang Hu is currently a Professor of Intelligent Systems and Robotics in the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. He received Ph.D. in Mechanical Engineering from University of Florida. His research interests include optimization and control, game theory, and learning algorithms, with applications to robotics and smart city systems. He was a recipient of several awards, including the Best Paper in Automation Award in the 14th IEEE International Conference on Information and Automation, the Best Paper Award in the 36th Chinese Control Conference, and the Best Paper Award in the 4th Asia Pacific Conference of the Prognostics and Health Management Society. He currently serves as Associate Editor for IEEE Transactions on Control Systems Technology and IEEE Transactions on Automatic Control.
Wentworth Institute of Technology, Boston, MA USA
Biography: Dr. En-Bing Lin is a full Professor and Associate Dean of the School of Computing and Data Science at Wentworth Institute of Technology, USA. He is a former mathematics department chair at the University of Toledo and Central Michigan University. He has taught and visited at several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his Ph. D. in Mathematics from Johns Hopkins University. His research interests include Neural Network, Artificial Intelligence, Data Analysis, Image Processing, Information Theory, Bioinformatics, Applied and Computational Mathematics, Wavelet Analysis and Applications, and Mathematical Physics. Dr. Lin serves on the editorial boards of multiple mathematics and computational journals. He has contributed to numerous academic committees for regional and national associations, most recently serving as the conference chair of the fourth International Conference on Information Communication and Software Engineering and the ninth International Conference on Mathematics and Artificial Intelligence, May 2024 as well as the sixth Asia Conference on Machine Learning and Computing, July 2024. Dr. Lin has been recognized with numerous honors and awards, including the Central Michigan University Distinguished Service Award, along with various research, travel, and education grants. He is also a member of IEEE, SIAM, AMS, and ACM.
Speech Title: The Dynamic Fusion of AI, Applied Mathematics, and Granular Computing
Abstract: This presentation explores the dynamic fusion of AI, Applied Mathematics, and Granular Computing, showcasing their synergistic relationship in driving innovation and addressing complex challenges. We highlight several projects illustrating these overlaps, emphasizing the fundamental role of rough set theory (RST) in processing information systems and problem-solving within big data analytics. By incorporating RST and fuzzy set theory, granular computing establishes analytical frameworks for sets and their attributes, enabling precise topological and computational analyses. We identify inconsistencies and errors in classical approximation models and propose predictive intelligence as a solution for informed decision-making, data analytics, and forecasting. Leveraging neural networks and wavelet analysis, particularly neurowavelet methods, enhances predictive accuracy and captures the dynamics of time series in the time-frequency domain. Our exploration includes the integration of Granular Computing (GRC) and Topology-Based Approximate Arithmetic, presenting a novel approach for improving the precision and efficiency of cognitive computing systems. GRC, inspired by human cognition, employs global precedence principles and the information processing hierarchy to handle data with greater nuance. We address challenges in standard numerical computing, such as rounding errors, by developing granular operations for addition and multiplication, forming semi-groups and topological fields. The proposed theories offer significant implications for AI and big data, enhancing data handling, reducing errors, and increasing precision in complex, dynamic data processing. Several case studies illustrate the practical applications of these concepts, while the introduction of BN-continuity in generalized approximation spaces, defined within binary neighborhood systems, aligns with Alexandroff spaces and extends AI applications. The presentation concludes by outlining future research directions, refining hierarchical structures in GRC, and further developing granular arithmetic models, inspiring interdisciplinary collaboration to tackle global challenges and drive advancements across several domains.
