The paper, “Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing,” co-authored with Professor Jionghua (Judy) Jin and Professor S. Jack Hu from the University of Michigan, was selected from 259 technical papers.
Shao’s research focuses on the development of a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.
MSEC is held annually and sponsored by the Manufacturing Engineering Division of ASME. The Best Paper Award is given to the author(s) of the most outstanding technical paper presented at MSEC.
Shao earned his PhD in mechanical engineering from the University of Michigan in 2016, and joined the MechSE department in August 2016. His research interests lie at the crossroads of digital manufacturing and big data analytics, and manufacturing system control and automation.