911ÖÆÆ·³§Â黨

Ìâ  Ä¿£ºData Fusion for Smart Manufacturing

±¨¸æÈË£ºDr. Jionghua(Judy)Jin£¨½ð¾¼»ª£©

Ö÷³ÖÈË£ºÉÛÐÂÓî½ÌÊÚ

ʱ  ¼ä£º2016Äê07ÔÂ22ÈÕÉÏÎç10:00µã

µØ  µã£ºÏȽøÖÆÔì´óÂ¥¶«Â¥A304

±¨¸æÈ˽éÉÜ£º

Jionghua (Judy) Jin is currently a professor in the Department of Industrial and Operations Engineering and the Director of 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 with broad applications in both manufacturing and service industries.  She has received numerous awards including the Forging Achievement Awards, the NSF CAREER Award, and the prestigious Presidential (PECASE) Award, ten Best Paper Awards since 2005 etc.  She is currently a Departmental Editor for IIE Transactions, and was Vice President of INFORMS and the President of QCRE division in IIE. She is a Fellow of IIE, a Fellow of ASME, an elected senior member of ISI, a senior member of ASQ, and a member of IEEE, INFORMS, and SME.

±¨¸æÕªÒª£º

Big data collected by product lifecycle management (PLM) systems provide unprecedented opportunities and research challenges for achieving smart manufacturing. The development of novel data analytics methodologies is highly demanded and will provide a long lasting impact in manufacturing industry. Data fusion research through integrating data mining, computer simulations, sensor fusion, optimization and decision making, represents one of the frontiers in this emerging area. This talk will provide an overview of the characteristics and the need of data fusion research for smart manufacturing. The basic concepts of data fusion research will be introduced with the emphasis on promoting the integration of disparate methodologies into a cohesive entity to enable optimal decisions for smart manufacturing. Some examples of methodological developments and their applications will be discussed in order to demonstrate the need for multidisciplinary integration efforts.

»¶Ó­²Î¼Ó£¡

Êý×ÖÖÆÔì×°±¸Óë¼¼Êõ¹ú¼ÒÖصãʵÑéÊÒ

2016Äê7ÔÂ15ÈÕ