Please use this identifier to cite or link to this item: https://doi.org/10.1016/B978-0-444-59506-5.50137-1
Title: A Graphic Processing Unit (GPU) Algorithm for Improved Variable Selection in Multivariate Process Monitoring
Authors: Chan, L.M.
Srinivasan, R. 
Keywords: Compute Unified Device Architecture (CUDA)
Genetic Algorithm
Graphics Processing Unit (GPU) parallel computing
Variable Selection
Issue Date: 2012
Source: Chan, L.M.,Srinivasan, R. (2012). A Graphic Processing Unit (GPU) Algorithm for Improved Variable Selection in Multivariate Process Monitoring. Computer Aided Chemical Engineering 31 : 1532-1536. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-444-59506-5.50137-1
Abstract: Process monitoring is extremely important for producing high quality product and at the same time ensuring safe working environment in chemical process industry. Recently, it has been shown that selection of an appropriate subset of variables can improve the monitoring performance. The main contribution of this work is the development of a parallel version of the Genetic Algorithm-Principal Component Analysis algorithm which was proposed by Ghosh et al. [2] for variable selection. The developed algorithm has been implemented using NVIDIA's Compute Unified Device Architecture, CUDA parallel computing platform. Experimental results show that the proposed parallel approach is 12 times faster than the original serial code when applied to the Tennessee Eastman challenge problem. © 2012 Elsevier B.V.
Source Title: Computer Aided Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/54229
ISSN: 15707946
DOI: 10.1016/B978-0-444-59506-5.50137-1
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