基于图模型的数据驱动建模与控制在智能制造系统中的应用
日期：2023/12/14 - 2023/12/14
学术讲座：基于图模型的数据驱动建模与控制在智能制造系统中的应用
主讲人：Dr. Chao Wang, Assistant Professor, Department of Industrial and Systems Engineering, University of Iowa
时间：2023年12月14日上午9:00-10:30
地点: 线上讲座，飞书链接：https://vc.feishu.cn/j/692239288
讲座摘要
Information revolution is turning modern engineering systems into smart and complex networks. Examples of such systems include GM’s OnStar® tele-service system and the InSite® tele-monitoring system from GE. The unprecedented data availability in these systems provides significant opportunities for data analytics. On the other hand, the tremendous data volume and types also reveal critical challenges. First, the high dimensional data with heterogeneity often poses difficulties in sharing common information within/across similar units/processes in the smart and connected systems. Moreover, the non-Gaussian data and non-linear relationship among various units impede the quantitative description of the inter-relationship of processes. In this talk, we establish a data analytic framework based on transfer/multitask learning that can rigorously reveal commonalities among heterogeneous systems and apply such information to benefit the learning of startup systems. This framework leverages on graphical modeling and Bayesian theory to build a unified data-driven methodology. Further, a non-parametric joint distribution approximation method based on Copula fitting is introduced to quantify the interactions within and across systems. The approximation results will serve for system control under heterogeneous noise environment. These methodologies are validated using numerical studies and real-world data from multistage manufacturing processes.
主讲人简介
Dr. Chao Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Iowa. He received his B.S. from the Hefei University of Technology in 2012, and M.S. from the University of Science and Technology of China in 2015, both in Mechanical Engineering, and his M.S. in Statistics and Ph.D. in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2018 and 2019, respectively. His research interests include statistical modeling, analysis, monitoring, and control for complex systems. His research is supported by various federal funding agencies such as NSF, DoD, DoE, and DoT. He is a recipient of Outstanding Young Manufacturing Engineer Award from SME, Best Paper Award from IISE Transactions, and several Best Paper Awards/Finalists from Quality, Statistics, and Reliability (QSR) Section and Data Mining (DM) Section at INFORMS Annual Conference. He is an Associate Editor of the Journal of Intelligent Manufacturing, and a member of INFORMS, IISE, and SME.