In recent years, deep learning caves out a research wave in machine learning. With its outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. This talk will introduce a fuzzy generative deep learning algorithm and a novel broad learning systems. A fuzzy generative learning -- Fuzzy Restricted Boltzmann Machine (FRBM) -- is developed by replacing real-valued weights and bias terms with symmetric triangular fuzzy numbers (STFNs) or Gaussian fuzzy numbers and corresponding learning algorithms. A theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. The second part of the talk is to discuss a very fast and efficient discriminative learning -- “Broad Learning”. Without stacking the layer-structure, the designed neural networks expand the neural nodes broadly and update the weights of the neural networks incrementally when additional nodes are needed and when the input data entering to the neural networks continuously. The designed network structure and learning algorithm are perfectly suitable for modeling and learning big data environment. Experiments results in MNIST and handwriting recognition and NORB database indicate that the proposed BLS significantly outperforms existing deep structures in learning accuracy and generalization ability.

C. L. Philip Chen is currently the Dean of the Faculty of Science and Technology, University of Macau, Macau, China and a Chair Professor of the Department of Computer and Information Science since 2010. He worked at U.S. for 23 years as a tenured professor, a department head and associate dean in two different universities. Dr. Chen's research areas are in systems, cybernetics and computational intelligence. He is a Fellow of the IEEE and AAAS. He was the President of IEEE Systems, Man, and Cybernetics Society (SMCS) (2012-2013). Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-).

Dr. Chen has been an Associate Editor of many IEEE Transactions, and currently he is an Associate Editor of IEEE Trans on Fuzzy Systems, IEEE Trans on Cybernetics, and IEEE/CAA Automatica Sinica. He is the Chair of TC 9.1 Economic and Business Systems of IFAC. He is also a Fellow of CAA and Fellow of HKIE and an Academician of International Academy of Systems and Cybernetics Science (IASCYS). In addition, he is an ABET (Accreditation Board of Engineering and Technology Education, USA) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.

Dr. Chen has received Outstanding Electrical and Computer Engineering Award in 2016 from his alma mater, Purdue University, West Lafayette, where he received his Ph.D. degree in 1988, after he received his M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985.

Despite the visions of a smart, connected, unmanned and continuously operating factory in every neighborhood, enabled by recent advances in robotics, IoT, machine learning, and cloud automation and manufacturing, most of today's smaller factories are far removed from this vision. Humans still do most of the parts fitting, assembly, inspection, and testing. In this talk I will examine some of the technological and economic factors behind this reality. I will also argue that a paradigm shift toward minimalism is what is needed: Simpler, lower-cost automation devices that are flexible and easily reconfigurable, driven by advanced algorithms and software, offer the greatest promise of bringing practical automation to today's manufacturing and assembly factories. I will outline what existing technologies and methods can be leveraged for immediate impact, and what some of the near- and longer-term technical challenges are that must be overcome. In particular, open problems in robot motion control, and also automated inspection and testing methods that leverage recent advances in machine learning, will be discussed.

Frank Park received his B.S. in EECS from MIT in 1985, and Ph.D. in applied mathematics from Harvard University in 1991. He joined the mechanical and aerospace engineering faculty at the University of California, Irvine in 1991, and since 1995 he has been professor of mechanical and aerospace engineering at Seoul National University, where he is currently serving as department chair since June 2017.

His research interests are in robot mechanics, planning and control, vision and image processing, machine learning, and related areas of applied mathematics. He has been an IEEE Robotics and Automation Society Distinguished Lecturer, and received best paper awards for his work on visual tracking and parallel robot design. He has served on the editorial boards of the Springer Handbook of Robotics, Springer Advanced Tracts in Robotics (STAR), Robotica, and the ASME Journal of Mechanisms and Robotics. He has held adjunct faculty positions at the NYU Courant Institute and the Interactive Computing Department at Georgia Tech, and is currently adjunct professor at the Robotics Institute at HKUST. He is a fellow of the IEEE, current editor-in-chief of the IEEE Transactions on Robotics, developer of the EDX course Robot Mechanics and Control I, II, and co-author (with Kevin Lynch) of Modern Robotics: Mechanics, Planning and Control (2017 Cambridge University Press).

Research on the walking robots has been one of key topics in robotics for a long time. In recent years, many legged robots were developed in the world, which of them achieved great progress and received much attention from the robotic field. The most important challenging issues are the design and human robot Interaction control of the legged robots. This speech will introduce our research on both mechanism design and real time control of the 6-legged parallel-parallel robots for the moving and manufacturing integration, which include the following issues: design process of type synthesis for legged robots by GF set theory, real-time operating system for legged robots, hexapod robot with safe riding capability, walking based on force sensing., obstacle avoidance with both vision and F/T sensor, walking upstairs by vision, human-robot interactive assembly based on F/T sensor, manufacturing based on F/T sensor, locked door opening based on F/T sensor for legged robots, and so on.

Feng Gao was born on Dec. 21, 1956 in Jiujiang City of Jiangxi Provence, P. R. of China. He got his Ph.D. in mechanical engineering from the Beijing University of Aeronautics and Astronautics in 1991 and his Master in mechanical engineering from the Northeast Heavy Machinery Institute, China in 1982. From 1995 to 1997, he was a postdoctoral research associate in the School of Engineering Science at Simon Fraser University, Canada. Dr. Feng has been serving as an Associate Editor of Mechanism and Machine Theory and the ASME Journal of Mechanisms and Robotics since 2008 and the ASME Journal of Mechanical Design since 2012, and the General Member of the ASME Mechanisms and Robotics Committee since 2012.

Dr. Feng gave the Keynote Speeches on the conferences of the ASME 2012 and IFToMM 2015, respectively. He won the 2013 China National Natural Science Award because of his contributions in parallel mechanism design and the 8 items of awards from the provincial science and technology invention prizes in China. 2014. Dr. Gao won 2014 ASME Leonardo Da Vinci Award for his invention of parallel manipulators.

Dr. Feng chief research domain is the parallel robots. The major achievements obtained include the design theory, invention and application of the parallel robots. In the theory aspect, he proposed the GF Set Theory for the type synthesis of parallel robotic mechanisms, the evaluating performance criteria and the physical model of the solution space for dimensional designing of parallel robotic mechanisms. In the application aspect, he Invented and Designed many kinds of the robots and machines with parallel mechanisms for heavy load applications He published 3 books and 288 papers. The 120 invention patents were authorized in China.