Mechatronics Considerations for Assisting Humans
Mechatronics technologies are now steadily penetrating in our daily lives. We are surrounded by mechatronic products and interact with them in many ways. In particular, mechatronics devices may potentially improve the quality of life of elderly people and patients with impairments. In this talk, several key technologies that we have developed for assisting such people in walking are introduced. These technologies include sensing technologies for identifying the intent of human, decision making algorithms to decide the right amount of assistance to the human and actuation technologies to provide forces and torques to selected human joints. In the design of mechatronic devices interacting with humans, the dynamics of human is an important element and the compatibility between machine and human must be optimized. If the device is interacting with normal and healthy humans, the design may take advantages of robust and intelligent controllability of a human. In case of assistive devices for elderly and people with impairments, such approaches will not be appropriate, and the controller is required to be predictable, precise, robust and intelligent. It is desired to have zero impedance for actuators to realize an ideal force mode actuation. Otherwise, a human will have to make additional efforts to overcome the undesirable resistance. We will present how the Flexible Joint Actuators (FJA) may be controlled to act as a zero impedance actuator. Also, the assistive systems require a means for detecting human intention and monitoring the current status of health or safety. Various sensor technologies are available for this purpose, e.g. EMG sensors, joint angle sensors and so on. For patients and elderly people, however, it is desired that sensors are easy to use and yet reliable and capable to generate relevant information. We will present the idea of smart shoes, which measure the distribution of foot pressures for robust estimation of phases in a human gait and detection of abnormalities. Based on the estimated phase, the control algorithm is adapted for most effective assistance of the user. This talk is based on research by Kyoungchul Kong and Joonbum Bae.
Prof. Masayoshi Tomizuka
Masayoshi Tomizuka holds the Cheryl and John Neerhout, Jr., Distinguished Professorship Chair in the Mechanical Engineering Department of the University of California at Berkeley. He received his B.S. and M.S. degrees in Mechanical Engineering from Keio University, Tokyo, Japan and his Ph. D. degree in Mechanical Engineering from the Massachusetts Institute of Technology in February 1974. He joined the faculty of the Department of Mechanical Engineering at the University of California at Berkeley in 1974. He served as Vice Chair of Mechanical Engineering from December 1989 to December 1991 and from July 1995 to December 1996. He also served as Director of Engineering Systems Research Center of the College of Engineering from July 1999 to August 2002. He served as Program Director of the Dynamic Systems and Control Program at the National Science Foundation from September 2002 to December 2004. At UC Berkeley, he teaches courses in dynamic systems and controls. His current research interests are optimal and adaptive control, digital control, signal processing, motion control, and control problems related to robotics, machining, manufacturing, information storage devices and vehicles. He has published more than 400 papers in archival journals and refereed conference proceedings. He has supervised about 80 Ph. D. students to completion. Many of his students teach at national and international academic institutions and others work as leaders in various industries. He served as Technical Editor of the ASME Journal of Dynamic Systems, Measurement and Control, J-DSMC (1988-93), Editor-in-Chief of the IEEE/ASME Transactions on Mechatronics (1997-99) and Associate Editor of the Journal of the International Federation of Automatic Control, Automatica (1993-99). He was General Chairman of the 1995 American Control Conference, and served as President of the American Automatic Control Council (AACC) (1998-99). He is a Fellow of the ASME, the Institute of Electric and Electronics Engineers (IEEE) and the Society of Manufacturing Engineers. He received the DSCD Outstanding Investigator Award (1996), the Pi Tau Sigma-ASME Charles Russ Richards Memorial Award (1997), the Oldenburger Medal (2002) and the John R. Ragazzini Award from AACC (2006).
Compliant Mechanisms for MEMS and Flexonics
A continuum compliant mechanism transmits applied forces from specified input ports to output ports by elastic deformation of its comprising materials, fulfilling required functions analogous to a rigid-body mechanism. It has a large range of applications in both micro and macro domains. This presentation describes a level-set method for designing monolithic mechanisms with distributed compliance and/or made of multiple materials. Central to the method is a level-set model that precisely specifies the distinct material regions and their sharp interfaces as well as the geometric boundary of the structure, capable of performing topological changes and capturing geometric evolutions at the interface and the boundary.
Techniques for eliminating de facto hinges and for geometric control in the design are discussed, aiming at producing more reliable compliant mechanism designs for MEMS devices. We further discuss the intrinsic deficiencies in the widely used "spring model" and propose a new formulation considering the "characteristic stiffness" of the mechanism. The result is a design with highly even-distributed compliance and a more desirable characteristic, which uniquely distinguishes our method. These methods are demonstrated with benchmark examples of both structure optimization and compliant mechanism optimization. The compliant mechanisms are intended for the use in automated assembly of hybrid MEMS with self-alignment techniques to eliminate tight positioning requirements.
Prof. Michael Y. Wang
Michael Yu Wang is a Professor at the Chinese University of Hong Kong, after ten years with the Department of Mechanical Engineering, University of Maryland. He has numerous professional honors-National Science Foundation Research Initiation Award, 1993; Ralph R. Teetor Educational Award from Society of Automotive Engineers, 1994; LaRoux K. Gillespie Outstanding Young Manufacturing Engineer Award from Society of Manufacturing Engineers, 1995; Boeing-A.D. Welliver Faculty Summer Fellow, Boeing, 1998; Distinguished Investigator Award of NSFC; Chang Jiang (Cheung Kong) Scholars Award from the Ministry of Education of China and Li Ka Shing Foundation (Hong Kong). He received the Kayamori Best Paper Award of 2001 IEEE International Conference on Robotics and Automation (with D. Pelinescu), the Compliant Mechanisms Award-Theory of ASME 31st Mechanisms and Robotics Conference in 2007, and Research Excellence Award (07-08) of Faculty of Engineering of CUHK. He is a Senior Editor of IEEE Trans. on Automation Science and Engineering, and served as an Associate Editor of IEEE Trans. on Robotics and Automation and ASME Journal of Manufacturing Science and Engineering. He is a Distinguished Lecturer of IEEE Robotics and Automation Society (2006-2009). His research interests include computational design and optimization of solids, precision engineering, and electronic and photonic manufacturing, with over 200 technical publications in these areas. He received his Ph.D degree from Carnegie Mellon University (1989). He is a Fellow of ASME, HKIE, and IEEE.
Knowledge Discovery from Networks
Nowadays, network becomes the engine of scientific research activities in 21st century. For example, a Web search engine is something to do with networked data mining and knowledge discovery from networks in deed. Networks interact with one another and are recursive. We have come to grasp the important knowledge of networks.
Network is the key to representing the complex world around us. Small changes in the topology, affecting only a few of the nodes, can open up hidden doors, allowing new possibilities to emerge. While network mining is considered in my talk, it is always stressed and focused on a kernel idea, i.e. topology first, mainly concerning the self-organization, self-similarity and emergency features.
Taking network topology as a novel approach of knowledge representation, we discuss how to mine typical topology patterns from real world networks at multi-scale, to evaluate node importance for node-ranking, to evaluate edge importance for edge-ranking, and to discover the membership for different communities in a network as well.
Brain science has achieved a great success on molecule-level and cell-level research; however, there is still a long way to go for cognitive function of a brain as a whole. How can we understand the non-linear function of a brain? How does the left brain (with the priority of logic thinking) cooperate with the right brain (with the priority of visual thinking)? How far away for "von-Neumann-style" computer architecture ? May the future computer architecture consist of dual core, one for logic thinking and the other for visual thinking, which correlate each other all the time? May the future operating systems are developed under the mechanism of "growth by preferential attachment"? I am interested in all these questions in my talk.
Prof. Deyi Li
Deyi Li, was born in 1944 in Jiangsu, China, He graduated at the Electronic Engineering Dept.,South East Univ. in 1967, received his PhD in Computer Science Dept., Heriot-Watt Univ. Edinburgh UK in 1983. He was elected as the member of Chinese Academy of Engineering in 1999, the member of Eurosian Academy of Science in 2004 respectively. At present, he is a professor in Tsinghua Univ., the director at Dept. of Information Science, National Natural Science Foundation of China, the vice president of both Chinese Institute of Electronics and Chinese Association of Artificial Intelligence. He has published over 100 papers and 4 books, owned Premium Award given by IEE Headquarters 1984/85, and the IFAC world congress outstanding paper 1999, currently interested in data mining, artificial intelligence with uncertainty, soft computing, and cognitive physics.