应哈尔滨工业大学理学院邀请，韩国首尔国立大学DONGIL CHO教授等8位著名学者将于近期访问理学院并做学术报告。欢迎感兴趣的老师和同学参加。
具体报告时间、地点、题目、摘要及报告人简介如下：
报告时间：2018年1月14日（星期日） 8:00
报告地点：一校区格物楼503室
1、报告题目：Ion Trap Based Quantum Technology
摘要：Quantum information processing is a novel information processing method which encodes information into a quantum system instead of conventional digital electronics. By utilizing unique characteristics in the quantum regime, including superposition, entanglement, and teleportation, the quantum technology can be a disruptive technology in information processing. To build physical quantum platforms, a number of approaches are being developed. Among these, ion traps are considered as a promising architecture, because of the long coherence time, ideal isolation from the surroundings, and capability of individual qubit manipulations. In this talk, ion trap based quantum technology is introduced. It includes a brief explanation of a background of quantum technology, basic principles of ion traps, MEMS-based approaches for constructing ion trap systems, and related researches performed at Seoul National University.
主讲人简介：Prof. Dong-Il “Dan” Cho received the B.S.M.E. degree from Carnegie-Mellon University, Pittsburg, PA, and the M.S and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge. From 1987 to 1993, he was an Assistant Professor at Princeton University, Princeton, NJ. Since 1993, he has been a Professor in the Department of Electrical and Computer Engineering at Seoul National University, Seoul, Korea. He is the author/coauthor of more than 120 international journal articles. He is the holder/coholder of 29 US patents and 82 Korean patents. He has served on the editorial board of many international journals. Currently, he is Senior Editor of the IEEE Journal of MEMS and IFAC Mechatronics. He was the President of ICROS and BOG Member of IEEE CSS, and is currently Vice President of IFAC, Chair of the Technical Board of IFAC, and AdCom Member of IEEE EDS. He is an elected Senior Member of National Academy of Engineering of Korea.
2、报告题目：Learning Control - Ideas and Problems in Adaptive Fuzzy Control
摘要：Intelligent control is a promising way of control design in recent decades. Intelligent control design usually needs some knowledge of the system considered. However, such knowledge usually may not be available. Learning becomes an important mechanism for acquiring such knowledge. Learning control seems a good idea for control design for unknown or uncertain systems. To learn controllers is always a good idea, but somehow like a dream. It is because learning is to learn from something. But when there is no good controller, where to learn from, Nevertheless, there still exist approaches, such as adaptive fuzzy control, that can facilitate such an idea. It is called performance based learning (reinforcement learning and Lyapunov stability). This talk is to discuss fundamental ideas and problems in one learning controller -- adaptive fuzzy control. Some deficits of such an approach are discussed. The idea is simple and can be extended to various learning mechanisms. In fact, such an idea can also be employed in various learning control schemes. If you want to use such kind of approaches, those issues must be considered in your study.
主讲人简介：Prof. Shun-Feng Su received the B.S. degree in electrical engineering, in 1983, from National Taiwan University, Taiwan, R.O.C., and the M.S. and Ph.D. degrees in electrical engineering, in 1989 and 1991, respectively, from Purdue University, West Lafayette, IN. He is now a Chair Professor of the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, R.O.C. He is an IEEE Fellow and CACS fellow. He has published more than 200 refereed journal and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include computational intelligence, machine learning, virtual reality simulation, intelligent transportation systems, smart home, robotics, and intelligent control. Dr. Su is very active in various international/domestic professional societies. He is now the past president of the International Fuzzy Systems Association. He now is also in the Boards of Governors of IEEE Systems, Man, and Cybernetics Society. He also serves as a board member of various academic societies. Dr. Su also acted as General Chair, Program Chair, or various positions for many international and domestic conferences. Dr. Su currently serves as Associate editors of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, and IEEE Access, a subject editor (Electrical Engineering) of the Journal of the Chinese Institute of Engineers, and the Editor-in-Chief of International Journal of Fuzzy Systems.
3、报告题目：Applications of Computational Intelligence to Industry 4.0
摘要：Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies. It is a system-of-systems including cyber-physical systems, the internet of things, cloud computing and computational intelligence. Computational intelligence is one of the most important key elements for Industry 4.0. In this talk, I shall share some personal thoughts and perspectives on topics such as Industry 4.0, smart machinery, etc., its spirit, technology and development, and technical level. I also shall share the research and development technologies and applications of our research team in the industry.
主讲人简介：Prof. Jyh-Horng Chou received the B.S. and M.S. degrees in Engineering Science from Cheng-Kung University, Taiwan, and the Ph.D. degree in Mechatronic Engineering from Sun Yat-Sen University, Taiwan. He is currently the Chair Professor of Electrical Engineering Department, Kaohsiung University of Applied Sciences, Taiwan. His research and teaching interests include intelligent systems and control, computational intelligence and methods, automation technology, robust control, and robust optimization. He was the recipient of the 2011 and 2014 Distinguished Research Award from the Ministry of Science and Technology of Taiwan, and the 2012 IEEE Outstanding Technical Achievement Award from the IEEE Tainan Section. Based on the IEEE CIS evaluation, his "Industrial Application Success Story" has got the 2014 winner of highest rank, thus being selected to become the first internationally industrial success story being reported on the IEEE CIS website. He is a Fellow of IEEE, IET, CACS, CSME and CIAE.
4、报告题目：Intelligent Control of Servo Motor Drives
摘要：Intelligent control systems including fuzzy and neural network systems have attracted the growing interest of researches and engineers in various scientific and engineering areas. This talk focuses on the development of several intelligent control systems for various applications of servo motor drives including hybrid supervisory control using recurrent fuzzy neural network controller for tracking periodic inputs, DSP-based cross-coupled synchronous control for dual linear motors via intelligent complementary sliding mode control, Intelligent double integral sliding-mode control for five-degree-of-freedom active magnetic bearing and Fault tolerant control for six-phase PMSM drive system via intelligent complementary sliding mode control using TSKFNN-AMF.
主讲人简介：Faa-Jeng Lin received B.S. and M.S. degrees in electrical engineering from National Cheng Kung University, Taiwan, and Ph.D. degree in electrical engineering from National TsingHua University, Taiwan, in 1983, 1985, and 1993 respectively. Currently, he is a Chair Professor at the Department of Electrical Engineering, National Central University, Taiwan. His research interests include AC motor drives, power electronics, renewable energies, smart grid, intelligent and nonlinear control theories. He is Associate Editor of IEEE Trans. on Fuzzy Systems and IEEE Trans. on Power Electronics. Moreover, he is the chair and principle investigator of Smart Grid Focus Center, National Energy Project Phase I and II in Taiwan. He received the Outstanding Research Awards from the National Science Council, Taiwan, in 2004, 2010 and 2013 and the Outstanding Professor of Engineering Award in 2016 from the Chinese Institute of Engineers, Taiwan. He is also an IEEE Fellow.
5、报告题目：Intelligent Adaptive Model Predictive Control Methods with Their Applications to Industrial processes and Intelligent Mobile Robots
摘要：By incorporating the merits of fuzzy modeling and recurrent fuzzy neural networks, this talk will present you four new adaptive predictive control methods of a class of nonlinear discrete-time time-delay systems not only for guaranteed stability but also for precise setpoint tracking and disturbance rejection. The four methods are a generalized predictive control method using recurrent fuzzy neural networks, an intelligent adaptive two-degrees-of-freedom control by combining a Takagi-Sugeno-Kang (TSK) type recurrent fuzzy neural network adaptive inverse model feedforward controller with a stochastic adaptive model reference predictive controller an adaptive predictive proportional-integral-derivative (PID) control approach by utilizing recurrent wavelet neural networks, and an adaptive predictive PID control via fuzzy wavelet neural networks. Through many simulations and experiments, the four methods are shown effective and useful. Last but not least, adaptive model predictive control methods with deep reinforcement learning are briefly introduced and shown effective for intelligent vehicle and mobile robots.
主讲人简介：Prof.Ching-Chih Tsai received the MS degree in Control Engineering from National Chiao Tung University, Hsinchu, Taiwan, and the Ph.D degree in Electrical Engineering from Northwestern University, Evanston, IL, USA, in 1986 and 1991, respectively. Dr. Tsai has published more than 500 technical papers, and seven patents in the fields of intelligent control systems, robotics, automation technology, and industrial applications. He is respectively the recipients of the Third Society Prize Paper Award from IEEE Industry Application Society in 1998, and the IEEE Most Active SMC TC Award in 2012 and many best paper awards from many international conferences technically supported by IEEE. He has served as the associate editors of IJFS and IEEE Transactions on SMC: Systems. Recently, he will serve as the Editor in Chief for a new international robotics journal called “iRobotics”. He has been evaluated as a Fellow of IEEE, IET and CACS, respectively.
6、报告题目：强化学习概述及其在IRIS实验室应用范例
摘要：强化学习演算法中奖惩函数的设计通常都是目标为导向的透过代理人以尝试错误的方法来学习完成目标，但当我们要将其应用在真实环境中时，状态如何的切割变得很难去决定。为了解决遇到的问题，本实验室先提出以决策树为基础的自适应性分割状态空间演算法。于此同时，我们也提出了融合强化学习以及闸门网路的演算法，由闸门网路融合过去已学习的策略可以比一开始没运用旧有策略的机器人更快得到学习效果。进而，研究更导向透过记忆不同的示范动作并且可以将其记忆成的基本动作再重现的方式，使机器人能够模仿示范者的行为。在模仿的过程中，系统将撷取人体骨架的关节经过转换对应成机器人各个关节，但其关节资讯及架构和真人不同，捕捉到的姿态无法直接对应到机器人的姿态而导致姿态法平衡，所提出的架构可使机器人自行学习调整静态重心，来补足这些问题。当机器人具有重现基本动作能力后，应具有动作精练化的能力，因此提出从示范动作中获得强化学习法中萃取出奖惩资讯，以此达到控制最佳化。为了解决这样的问题，使用逆向强化学习演算法，获取示范者可能的意向-奖惩函数。但传统逆向演算法在复杂的问题中，往往难以挑出适当的奖惩函数索引，有时会直接采用整个状态空间作为奖惩函数索引。所以我们提出基于关键状态及以Adabooster的逆向强化学习演算法，以求出简洁、有意义的奖惩函数，再利用强化学习演算法精练基本动作能力。
主讲人简介：黄国胜教授现任职于国立中山大学电机系，同时，任高雄医学大学合聘教授。于1993年获美国西北大学电脑工程博士学位，后被国立中正大学电机系延揽回国任教并继续从事机器人科学技术方面的研究。曾担任中正大学电算中心组长、代主任、电机系系主任、光机电整合研究所所长、国科会自动控制学门规划及复审委员、自动化学门复审委员。由于其学术表现受认同，因此成为中国自动控制学会会士、欧洲电子电机学会(IET)院士、IEEE Trans. on Cybernetics编辑、以及IEEE/ACMTrans. on Mechatronics编辑、IEEE/CAA Journal of AutomaticaSinica编辑，也受邀为中国上海交通大学荣誉客座教授、中国西北工业大学荣誉客座讲座教授。研究领域包括机器人路径规划、机器足球员系统、强化合作学习系、以及群组机器人任务合作。
7、报告题目：Leader-Follower Type Adaptive Formation-Based Transportation by Cooperation of Two Mobile Robots
摘要：The existing leader-follower formation control algorithms use the leader’s velocity information. In practice, it is not necessarily to equip all robots with velocity sensors and feedforward neural networks is used for estimating the uncertainties containing leader’s velocity information. In this research, a decentralized control is adopted because huge computational power and communication capacity of controller are not required in this structure. A leader-follower type formation controller with adaptive law for mobile robots cooperative transportation without using extra sensor. The stability is proven by using Lyapunov theorem. Furthermore, the leader robot can also be replaced by a human or other, because that only the relative position and orientation are considered in this scheme. Finally, the validity of this scheme is supported by many computer simulations and experiments.
主讲人简介：Prof.Mei-Yung Chen received the B.S. degree from TamKang University in 1992, the M.S. degree from Chung Yuan Christian University in 1994, the Ph.D. degree from National Taiwan University in 2003. He is currently a professor of the Department of Mechatronic Engineering, National Taiwan Normal University, Taiwan. His areas of research interest include magnetic levitation, positioning and tracking, mechatronics, and control theory and its applications.
8、报告题目：Sharing of the Process of Inventions for Chen’s Electrical Unifying Approach
摘要："Chen’s Electrical Unifying Approach" (CEUA) is to use a simple and systematic method and formula invented by Professor Chung-Cheng Chen and the basic ability of addition, subtraction, multiplication and division to solve the electronic circuit experiment, circuit science, electronics, automatic control, power system, engineering mathematics, power electronics, biomedical electronic network control, electronic circuit design, and most of the topics in various fields and the hardware. It can be described as a kind of “student easy to learn and teacher easy to teach” innovative research method.
CEUA invented by professor Chung-Cheng Chen won the award for excellent industrial education Jinduo Award, the TV station broadcast and accepted the daily media interview with the headlines. The important features of CEUA are as follows: (1)Using the CEUA can design a simple design function wave generator, simply find the oscillator oscillation conditions and oscillation frequency; (2)One at the same time finds the amplifier voltage gain, current gain, input impedance, output impedance, Davidin equivalent voltage, Davidine equivalent resistance; (3) Simplifies the derivation of state equation of control system; (4) Easily derive the Inverse z-Transform; (5) Easily finds the complex block diagram transfer function. Parts of CEUA results have been printed in the SCI /EI international journals, and inspire students creativity.
主讲人简介：Prof. Chung-Cheng Chen received the Bachelor's degree from National Taiwan Normal University, Taiwan, in 1982, the Master's and Ph. D. degrees of electrical engineering from National Sun Yat-Sen University, Taiwan, in 1987 and 1994, respectively. He joined the faculty of the Department of Electrical Engineering, National Formosa University in 1989 as a lecturer. He received the Ph. D degree and became a professor there in 2000. Since 2008, he has joined the faculty of the Department of Electrical Engineering, National Chiayi University. He joined the faculty of the Department of Electrical Engineering, Hwa Hsia Institute of Technology in 2011 as a lecturer and invited as the dean of College of Electronic Engineering and Intelligent Manufacturing, City College of Dongguan University of Technology, China in 2017. His research interests include biomedical engineering (HIV and cancer), Neural Network, fuzzy control, nonlinear control theory and design, feedback linearization control, singularly perturbed system, signal processing and the Chen’s Electric Unifying Approach.
