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.