报告人：Prof. S Joe Qin（University of Southern California）时 间：5月28日09:30-10:30地 点：数学院南楼902摘 要：Manufacturing process operations collect and store massive data from routine operations with computers and information systems. The massive process data providea valuablebasis for decision-making and diagnosis of operations and control systems. In this talk we first provide a historical perspective on the process data analytics based on latent variables modeling methods and machine learning, and the objectives to distill desirable components or features from a mixture of measured data. These methods are then extended to modeling high dimensional dynamic time series data to extract the most dynamic latent variables.The extracted dynamic latent variables are best predicted from their past values. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods to extract features for process operations and control, leading to new perspectives on how process data are indispensable for process operations and control.
Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is the Professor at the Viterbi School of Engineering of the University of Southern California.Dr. Qin is a Fellow of IEEE and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, Chang Jiang Professor of Tsinghua University, National “Thousand Talent” Professor of China, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He is currently a Subject Editor for Journal of Process Control and a Member of the Editorial Board for Journal of Chemometrics. He has published over 140 papers in SCI journals or book chapters, with over 10,000 Web of Science citations and an associated h-index of 49. He has given over 40 invited plenary or keynote speeches and over 100 invited technical seminars worldwide. Dr. Qin’s research interests include process data analytics, machine learning, process monitoring and fault diagnosis, model predictive control, system identification, building energy optimization, multi-step batch process control, and control performance monitoring.