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ChangKyoo Yoo
Researcher at Kyung Hee University
Publications - 340
Citations - 9206
ChangKyoo Yoo is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Exergy & Renewable energy. The author has an hindex of 38, co-authored 308 publications receiving 7169 citations. Previous affiliations of ChangKyoo Yoo include Pohang University of Science and Technology & Ghent University.
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Nonlinear process monitoring using kernel principal component analysis
TL;DR: In this article, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed, which can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions.
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Statistical process monitoring with independent component analysis
TL;DR: The proposed monitoring method was applied to fault detection and identification in both a simple multivariate process and the simulation benchmark of the biological wastewater treatment process, which is characterized by a variety of fault sources with non-Gaussian characteristics.
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Statistical monitoring of dynamic processes based on dynamic independent component analysis
TL;DR: The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables to show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto- and cross-correlation of variables.
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Fault detection of batch processes using multiway kernel principal component analysis
TL;DR: The proposed batch monitoring method using multiway kernel principal component analysis (MKPCA) can effectively capture the nonlinear relationships among process variables in both off-line analysis and on-line batch monitoring.
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On-line monitoring of batch processes using multiway independent component analysis
TL;DR: On-line batch monitoring method with multiway independent component analysis (MICA), based on a recently developed feature extraction method, which provides more meaningful statistical analysis and on-line monitoring compared to MPCA because MICA assumes that the latent variables are not Gaussian distributed.