Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system
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oai:scholars.utp.edu.my:347422023-03-20T03:20:10Z http://scholars.utp.edu.my/id/eprint/34742/ Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system Nawaz, Muhammad Maulud, Abdulhalim Shah Zabiri, Haslinda 2021 Conference or Workshop Item NonPeerReviewed Nawaz, Muhammad and Maulud, Abdulhalim Shah and Zabiri, Haslinda (2021) Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system. In: E3S Web of Conferences. |
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Nawaz, Muhammad Maulud, Abdulhalim Shah Zabiri, Haslinda |
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Nawaz, Muhammad Maulud, Abdulhalim Shah Zabiri, Haslinda Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
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Nawaz, Muhammad Maulud, Abdulhalim Shah Zabiri, Haslinda |
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Nawaz, Muhammad |
title |
Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
title_short |
Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
title_full |
Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
title_fullStr |
Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
title_full_unstemmed |
Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
title_sort |
multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system |
publishDate |
2021 |
url |
http://scholars.utp.edu.my/id/eprint/34742/ |
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1761621136116809728 |
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13.222552 |