Abstract: 

In this talk, I will introduce a quantitative measure of partial observability for dynamical systems. For problems with very high dimensions and big data sets, the concept and theory of partial observability are developed using a computational approach. The theory and numerical methods are illustrated using several examples, including optimal sensor placement for data assimilation, PMU placement for power systems, and the detection of swarm coordination strategy of unmanned vehicles. 

Bio:

Professor Wei Kang received B.S. and M.S. degrees from Nankai University, China, in 1982 and 1985, respectively, and a Ph.D. degree from the University of California at Davis in 1991, all in mathematics. In 1991-1994, he held a faculty position at Washington University in St. Louis. Since 1994, he has been with the faculty of Applied Mathematics at US Naval Postgraduate School, where he is currently the department chair. He was a Director of American Institute of Mathematics (2008-2011) for business and international collaborations. He was a visiting scientist at Intel (2005) and a consultant of EPRI (2011-2012). He is a fellow of IEEE. Professor Kang’s research interests include computational mathematics and control systems, optimal control and estimation, bifurcations and normal forms, and control system applications.