Title: CPS Driven Control System
China has abundance of mineral resources such as magnesite, hematite and bauxite, which constitute a key component of its economy. The relatively low grade, the widely varying and complex compositions of the raw extracts, however, pose difficulties and challenges to processing industries, such as specialized equipment with excessive energy demands. One typical example is the energy intensive furnaces together with widely uncertain features of the extracts, which forms a hybrid complex system and makes the existing modeling, optimization and control methods have met only limited success. Currently, the mineral processing plants generally employ manual control and are known to impose greater demands on the energy, while yielding unreasonable waste and poor operational efficiency. The recently developed Cyber-Physical System (CPS) provides a new key for us to address these challenges. The idea is to make the control system of energy intensive equipment into a CPS, which will lead to a CPS driven control system.
This talk presents the syntheses and implementation of a CPS driven control system for energy-intensive equipment under the framework of CPS. The proposed CPS driven control system consists of four main functions: (I) setpoint control; (II) tracking control; (III) self-optimized tuning; and (IV) remote and mobile monitoring of operating conditions. The key in developing the above functions is the integrated optimal operational control method that is able to seamlessly integrate the setpoint control, tracking control and self-optimized tuning together. This talk introduces the integrated optimal operational control methods we proposed recently.
Hardware and software platform of CPS driven control system for energy-intensive equipment is then briefly introduced, which adopts embedded control system, wireless network and industrial cloud. It not only realizes the functions of computer control system using Distributed Control System (DCS) (PLS), for optimization and computer for abnormal condition identification and self-optimized tuning, but also achieves the functions of mobile and remote monitoring for industrial process.
Then, using fused magnesium furnace as an example, a hybrid simulation system for CPS driven control system for energy-intensive equipment developed by our team is introduced. The results of simulation experiments show the effectiveness of the proposed method that integrates the setpoint control, tracking control, self-optimized tuning and remote and mobile monitoring for operating condition in the framework of CPS.
The industrial application of the proposed CPS driven control system is also discussed. It has been successfully applied to the largest magnesia production enterprise in China, resulting in great returns. Finally, future research on the CPS driven control system is outlined.
Tianyou Chai received the Ph.D. degree in control theory and engineering in 1985 from Northeastern University, Shenyang, China, where he became a Professor in 1988. He is the founder and Director of the Center of Automation, which became a National Engineering and Technology Research Center and a State Key Laboratory. He is a member of Chinese Academy of Engineering, IFAC Fellow and IEEE Fellow, director of Department of Information Science of National Natural Science Foundation of China.
His current research interests include modeling, control, optimization and integrated automation of complex industrial processes.
He has published 200 peer reviewed international journal papers. His paper titled Hybrid intelligent control for optimal operation of shaft furnace roasting process was selected as one of three best papers for the Control Engineering Practice Paper Prize for 2011-2013. He has developed control technologies with applications to various industrial processes. For his contributions, he has won 4 prestigious awards of National Science and Technology Progress and National Technological Innovation, the 2007 Industry Award for Excellence in Transitional Control Research from IEEE Multiple-conference on Systems and Control, and the 2017 Wook Hyun Kwon Education Award from Asian Control Association.
Title: On Data-Selective Learning
The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifies to improve the related statistical inference or if it consists of an outlier or a non-innovative entry.
In this presentation, we discuss some extensions of the existing adaptive filtering algorithms, and if time allows machine learning algorithms, enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data.
A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacrificing the estimation accuracy, while reducing the computational cost.
Paulo S. R. Diniz was born in Niterói, Brazil. He received the Electronics Eng. degree (Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree from COPPE/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P.Q., Canada, in 1984, all in electrical engineering.
Since 1979 he has been with the Department of Electronics and Computer Engineering UFRJ. He has also been with the Program of Electrical Engineering (the graduate studies dept.), COPPE/UFRJ, since 1984, where he is presently a Professor. He served as Undergraduate Course Coordinator and as Chairman of the Graduate Department. He has received the Rio de Janeiro State Scientist award, from the Governor of Rio de Janeiro state.
From January 1991 to July 1992, he was a Visiting Research Associate in the Department of Electrical and Computer Engineering of University of Victoria, Victoria, B.C., Canada. He also held a Docent position at Helsinki University of Technology (now Aalto University). From January 2002 to June 2002, he was a Melchor Chair Professor in the Department of Electrical Engineering of University of Notre Dame, Notre Dame, IN, USA. His teaching and research interests are in analog and digital signal processing, adaptive signal processing, digital communications, wireless communications, multi-rate systems, stochastic processes, and electronic circuits.
He has published over 100 refereed papers in journals and over 200 conference papers in some of these areas and wrote the textbooks ADAPTIVE FILTERING: Algorithms and Practical Implementation, Fourth Edition, Springer, NY, 2013, and DIGITAL SIGNAL PROCESSING: System Analysis and Design, Second Edition, Cambridge University Press, Cambridge, UK, 2010 (with E. A. B. da Silva and S. L. Netto), and the monograph BLOCK TRANSCEIVERS: OFDM and Beyond, Morgan & Claypool, New York, NY, 2012 (W. A. Martins, and M. V. S. Lima).
He has served as the Technical Program Chair of the 1995 MWSCAS held in Rio de Janeiro, Brazil. He was the General co-Chair of the IEEE ISCAS2011 and Technical Program co-Chair of the IEEE SPAWC2008. He has also served Vice President for region 9 of the IEEE Circuits and Systems Society and as Chairman of the DSP technical committee of the same Society. He is also a Fellow of IEEE and EURASIP. He has served as associate editor for the following Journals: IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing from 1996 to 1999, IEEE Transactions on Signal Processing from 1999 to 2002, and the Circuits, Systems and Signal Processing Journal from 1998 to 2002. He was a distinguished lecturer of the IEEE Circuits and Systems Society for the year 2000 to 2001, and of the IEEE Signal Processing Society in 2004. He also received the 2004 Education Award and the 2014 Charles Desoer Technical Achievement Award both from IEEE Circuits and Systems Society. He also holds some best-paper awards from conferences and an IEEE journal. He had served as a Regional Director of the IEEE Signal Processing Society from 2015 to 2017.
Prof. P. S. R. Diniz is a member of the National Academy of Engineering (ANE), and of the Brazilian Academy of Science (ABC).
Title: Passive circuit theoretic paradigm for digital modeling of nonlinear PDEs of classical physics
In this talk, we discuss how the circuit theoretic paradigm can be extended to include multidimensional and nonlinear passive systems to model a variety of phenomena from classical physics. While mechanical, electromagnetic, acoustic, and heat diffusion problems can be treated in this way in a relatively straightforward manner, the centerpiece of the talk will be the examination of the full Navier-Stokes equation (including heat flow) within this framework. All ingredients of lumped passive circuit theory are kept intact, but multidimensionality and simple nonlinear aspects are appropriately introduced to represent the nonlinear PDEs associated with the physical phenomena in circuit theoretic terms. The details of the feasibility of such mapping for viscous fluids have not been explicitly discussed in the open literature, which will be focus of this talk. If time permits, related magneto-hydrodynamic and relativistic fluid dynamic equations will also be undertaken in this context. Since such continuous domain multidimensional circuits are known to be internally passive, robust digital equivalents via identification with wave digital principles are feasible, and the methodology opens up a new strategy for numerical modelling and solution of a large class of partial differential equations of physical origin.
Dr. Sankar Basu is a program Director at the US National Science Foundation (NSF). He received his PhD from the University of Pittsburgh, and prior to NSF he was at the IBM T. J. Watson Research Center, and on the faculty of Stevens Institute of Technology. He has visited the Ruhr University, Bochum, Germany as an Alexander von Humboldt fellow, and the MIT Laboratory for Information and Decision Systems (LIDS) for extended periods. During 2012 he served as an Embassy Science Fellow in Berlin, Germany, and more recently has visited Wroclaw University of Science & Technology for short periods.
Other than his NSF responsibilities of managing research in the general area of micro- and nano-computing, Dr. Basu's own research interests has been in the analytical aspects circuits, systems and signal processing. At IBM, he worked on statistical machine learning, speech and multimedia data retrieval, and has extensively published on filter synthesis, image processing, nonlinear modeling techniques. An author/coauthor of over one hundred refereed publications including two special volumes on wavelets and filter banks, he holds 10 US Patents. He was keynote speakers at International Workshop on nD systems in 1998 as well as in 2000 held respectively in Lagow, and Czocha Castle, Poland, and more recently at the 34th IEEE Int. Conf. on Computer Design Phoenix, AZ, 2016.
Dr. Basu has organized, chaired sessions, and has been a panelist in many conferences in the areas of nanocomputing, circuits systems signal processing, and statistical learning theory. He initiated and was the general chair of the first IEEE International Conference on Multimedia & Expo (ICME) in 2000, for which he also served as the steering committee chair for several years. He was a co-organizer for the NATO Advanced Study Institute on Statistical Learning and Applications at the Katholieke University, Leuven, Belgium in 2002, and co-edited the book Advances in Learning Theory: Methods, Models, and Applications.
He was the Editor-in-Chief (EIC) for IEEE Transactions on Circuits and Systems (TCAS)-Part I: Regular Papers during 2006-2007 and was the EIC for the TCAS-part II: Express Briefs during 2004-2005. He has served in editorial capacities for several publications including the IEEE Transactions on Multimedia, the Journal of Applied Signal Processing of the European Association of Signal Processing, and the Journal of VLSI Signal and Image Processing published by the Kluwer Academic Publishers. He also served on the Editorial Board of IEEE Press during 2007-2010. At present, he serves on the editorial board of the Journal of Multidimensional Systems and Signal Processing published by Springer, and Proceedings of the IEEE.
In 2001, he was elected a Fellow of the Institute of Electrical and Electronic Engineers (IEEE) for his contributions to the 'theory and application of multidimensional circuits, systems and signal processing'. He is also an elected Fellow of the American Association for the Advancement of Science (AAAS) for contributions to 'fundamental techniques in circuits and systems theory and their applications in computational science and engineering including processing of multi-dimensional and statistical data'. In 2011 the Semiconductor Research Corporation (SRC) recognized him with an award for Enhancing the Mission of SRC and NSF through Collaboration.