学术报告通知“Control Designs that Enhance Perception by Climbing Information Gradients” by John Baillieul

受东北大学流程工业综合自动化国家重点实验室主任柴天佑院士邀请, IEEE控制系统协会前主席、IEEE TAC前主编、美国波士顿大学John Baillieul教授将在我校举行学术报告。欢迎广大师生踊跃参加!

Control Designs that Enhance Perception by Climbing Information Gradients

时间:2013年5月21日(周二)8:50-10:30
地点:东北大学综合楼710(会议室)
Biography:
John Baillieul's research deals with robotics, the control of mechanical systems, and mathematical system theory. His PhD dissertation, completed at Harvard University in 1975, was an early work dealing with connections between optimal control theory and what came to be called “sub‐Riemannian geometry”. After publishing a number of papers developing geometric methods for nonlinear optimal control problems, he turned his attention to problems in the control of nonlinear systems modeled by homogeneous polynomial differential equations. Such systems describe, for example, the controlled dynamics of a rigid body. His main controllability theorem applied the concept of finiteness embodied in the Hilbert basis theorem to develop a controllability condition that could be verified by checking the rank of an explicit finite dimensional operator. Baillieul’s current research is aimed at understanding decision making and novel ways to communicate in mixed teams of humans and intelligent automata. John Baillieul is a Fellow of IFAC, a Fellow of the IEEE and a Fellow of SIAM.
Abstract:
Motivated by problems arising in automated perception of digital images, the talk presents recent research on formal methods for describing the information content of spatially varying scalar fields defined on R^1,R^2, and R^3. The methods have been developed by combining analytical tools from differential topology and information theory. The concept of topological persistence is described, and a refined notion that we refer to as topological information utility is presented. The results will be shown to provide a theoretical framework for robotic search strategies that are capable of rapid discovery of topological features in a priori unknown differentiable random fields.