Development of Robust Design Using Compressed Data  
Author Chien-Chih Lin

 
Co-Author(s) Hao-Ting Wei; Chung-Shou Liao

 
Abstract In this study, we propose a new idea which uses "Compressive Sensing" to deal with robust design and efficiently recover sparse vectors that represent the coefficients of parameters. We also investigate how to recover the sparse vectors that represent the parameters more efficiently. We take a sufficient number of collective additive measurements using this framework through constructing a feasible measurement matrix. Moreover, we demonstrate its effectiveness that this idea would get an accurate solution even for a huge number of parameters used in robust design. The most important thing is that we leverage the two different fields and incorporate Compressive Sensing into the industrial product design problem.

 
Keywords compressive sensing, robust design , data compression
   
    Article #:  22032
 
Proceedings of the 22nd ISSAT International Conference on Reliability and Quality in Design
August 4-6, 2016 - Los Angeles, California, U.S.A.