题目：Data Fusion Research for System Innovation and Quality Improvement
Jionghua (Judy) Jin is currently a professor in the Department of Industrial and Operations Engineering and theDirectorof Manufacturing Engineering Program at the University of Michigan. She received her BS and MS in Mechanical Engineering at Southeast University (Nanjing) in 1984 and 1987, and her PhD in Industrial and Operations Engineering at the University of Michigan in 1999.
Dr. Jin’s research focuses on developing new data fusion methodologies for system innovation and quality improvement. Her research integratesadvanced statistics, signal processing, quality control and reliability, and system control and decision-making theories. She has received numerous awards including nice Best Paper Awards,the Forging Achievement Awards, the NSF CAREER Award, the prestigious Presidential Awards (PECASE) Award, etc. She is currently a Departmental Editor for IIE Transactions, and was Vice President of INFORMS (Institute for Operations Research and the Management Sciences) and the President of QCRE/IIE (Quality Control & Reliability Engineering Division of Institute of Industrial Engineers).She is a Fellow of IIE, a Fellow of ASME, an elected member of ISI, a senior member of ASQ, and a member of IEEE, INFORMS,and SME.
More information about Dr. JionghuaJin can be found at http://www-personal.umich.edu/~jhjin/.
Our current data rich environment throughout product lifecycle management provides unprecedented opportunities and research challenges forimprovingdesign and operationof complex manufacturing systems. The development of a novel data fusion methodology is currently very relevant, and will provide a long lasting future impact. Efforts in data fusion research have significantly advanced the methodology development in the areas like product designconsidering potential customer responses, quality improvement during system operations throughsensor fusion, rapid change detection, root cause diagnostics, system control, and maintenance planning. Data fusion, through integrating engineering domain knowledgewith advanced data analysis techniques from advanced statistics, signal processing, system control and decision making, represents one of the frontiers in the quality control research for improving complex manufacturing and service operations.
This talkwill provide an overview of my ongoing data fusion research. The basic concepts in data fusion research will be introduced with the emphasis placed on promoting the integration of disparate methodologies into a cohesive entity to enable effective decision-making for complex manufacturing system design and operations. Examples of methodological developments and their applications will be discussed in order to demonstrate the characteristics of data fusion research and to emphasize the need for multidisciplinary system integration efforts.