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Meeting and Seminar Archive:

Date:  April 12, 2010


Subject: Pervasive Learning Diagnosis and Management of Production Systems


Speaker: Julia Liu, Palo Alto Research Center


Abstract: Modern production systems are optimized for productivity, but component faults or deterioration often occur in practice.  Diagnosis and health management of a production system are thus important. They can be formulated as a statistical inference problem, where observations are obtained to update the knowledge regarding component conditions. Prior work often puts the production system in halt and switch to trouble-shooting/learning mode to gather observations; other work uses passive observations during production. In this talk, we introduce the novel paradigm of pervasive learning, which constructs informative production plans that simultaneously achieve production goals while uncovering additional information.  We show two concrete examples of pervasive learning (diagnosis of single-fault systems, and continuous model adaptation) and explain the information criteria that are used to select production plans. 


Speaker Bio: Julia Liu received her Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. In 2001, she joined Palo Alto Research Center (PARC) as a member of research staff. Her research interests include signal processing, statistical modeling and inference, and applications such as diagnosis and reasoning. She is the recipient of IEEE Signal Processing Society Best Young Author Paper Award of 2002. She has served as a guest editor of the IEEE Signal Processing Magazine.



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