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TITLE: Monitoring and Diagnosis of Complex Systems with Multi-stream High Dimensional Sensing Data
SPEAKER: Dr. Qingyu Yang, Research Fellow
ABSTRACT:
The wide deployment and application of distributed sensing and computer systems have resulted in multi-stream sensing data leading to both temporally and spatially dense data-rich environments, which provides unprecedented opportunities for improving operations of complex systems in both manufacturing and healthcare applications. However, it also brings out new research challenges on data analysis due to high-dimensional and complex temporal-spatial correlated data structure. In this talk, as an example of my research work, I will discuss a critical research issue on how to separate immeasurable embedded individual source signals from indirect mixed sensor measurements. In this research, a hybrid analysis method is proposed by integrating Independent Component Analysis and Sparse Component Analysis. The proposed method can efficiently estimate individual source signals that include both independent signals and dependent signals which have dominant components in the time or linear transform domains. With source signals identified, it is feasible to monitor each source signal directly and provide explicit diagnostic information.
Bio:
Dr. Qingyu Yang is currently a postdoctoral research fellow with the Department of Industrial & Operations Engineering at the University of Michigan-Ann Arbor. He received a M.S. degree in Statistics and a Ph.D. degree in Industrial Engineering from the University of Iowa in 2007 and 2008, respectively. He also held a B.S. degree in Automatic Control (2000) and a M.S. degree in Intelligent System (2003) from the University of Science and Technology University of China (USTC, China). His research interests include distributed sensor system, information system, and applied statistics. He was the recipient of the Best Paper Award from Industrial Engineering Research Conference (IERC) 2009.
