{"52906":{"#nid":"52906","#data":{"type":"event","title":"Monitoring and Diagnosis of Complex Systems with Multi-stream High Dimensional Sensing Data","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E Monitoring and Diagnosis of Complex Systems with\nMulti-stream High Dimensional Sensing Data \u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E Dr. Qingyu Yang\u003Cstrong\u003E, \u003C\/strong\u003EResearch\nFellow\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThe wide deployment and application of distributed sensing and computer\nsystems have resulted in multi-stream sensing data leading to both temporally\nand spatially dense data-rich environments, which provides unprecedented\nopportunities for improving operations of complex systems in both manufacturing\nand healthcare applications. However, it also brings out new research\nchallenges on data analysis due to high-dimensional and complex\ntemporal-spatial correlated data structure. In this talk, as an example of my\nresearch work, I will discuss a critical research issue on how to separate\nimmeasurable embedded individual source signals from indirect mixed sensor\nmeasurements. In this research, a hybrid analysis method is proposed by\nintegrating Independent Component Analysis and Sparse Component Analysis. The\nproposed method can efficiently estimate individual source signals that include\nboth independent signals and dependent signals which have dominant components in\nthe time or linear transform domains. With source signals identified, it is\nfeasible to monitor each source signal directly and provide explicit diagnostic\ninformation. \u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Qingyu Yang is currently a postdoctoral research fellow with the\nDepartment of Industrial \u0026amp; Operations Engineering at the University of\nMichigan-Ann Arbor. He received a M.S. degree in Statistics and a Ph.D. degree\nin Industrial Engineering from the University\n of Iowa in 2007 and 2008,\nrespectively. He also held a B.S. degree in Automatic Control (2000) and a M.S.\ndegree in Intelligent System (2003) from the University\nof Science and Technology University\nof China (USTC, China).\nHis research interests include distributed sensor system, information system,\nand applied statistics. He was the recipient of the Best Paper Award from \u003Cem\u003EIndustrial\nEngineering Research Conference\u003C\/em\u003E (IERC) 2009.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"Monitoring and Diagnosis of Complex Systems with Multi-stream High Dimensional Sensing Data","format":"limited_html"}],"field_summary_sentence":[{"value":"Monitoring and Diagnosis of Complex Systems with Multi-stream High Dimensional Sensing Data"}],"uid":"27187","created_gmt":"2010-02-15 11:01:22","changed_gmt":"2016-10-08 01:50:17","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-02-19T11:00:00-05:00","event_time_end":"2010-02-19T12:00:00-05:00","event_time_end_last":"2010-02-19T12:00:00-05:00","gmt_time_start":"2010-02-19 16:00:00","gmt_time_end":"2010-02-19 17:00:00","gmt_time_end_last":"2010-02-19 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"167318","name":"sensor"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}