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- W4220686905 abstract "Purpose The purpose of this paper is to propose a approach for data visualization and industrial process monitoring. Design/methodology/approach A deep enhanced t -distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t -stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph. Findings The proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring. Originality/value This paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t -SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed." @default.
- W4220686905 created "2022-04-03" @default.
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- W4220686905 date "2022-03-18" @default.
- W4220686905 modified "2023-09-28" @default.
- W4220686905 title "Industrial process data visualization based on a deep enhanced <i>t</i>-distributed stochastic neighbor embedding neural network" @default.
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- W4220686905 doi "https://doi.org/10.1108/aa-09-2021-0123" @default.
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