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- W4324119586 abstract "In the industrial process, the production units are normally linked sequentially with material and energy transformation at all times. Thus, the process variables collected are often spatio-temporal correlated. In this paper, we propose a multi-fusion correlationfeature learning method based on active-inert variable separation CNN (VS-CNN). It fully extracts and fuses the feature correlations for highly correlated active variables by using dilated convolution and 1*1 convolution. Moreover, it extracts the temporal correlations for low correlated inert variables by using one-dimensional convolution. In this way, the feature extraction of the model can be more effective for quality prediction. The proposed model is applied to the quality prediction of an industrial hydrocracking process to verify its effectiveness." @default.
- W4324119586 created "2023-03-15" @default.
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- W4324119586 date "2022-11-25" @default.
- W4324119586 modified "2023-10-16" @default.
- W4324119586 title "Quality prediction model for multi-fusion correlation feature learning method based on active-inert variable separation CNN" @default.
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- W4324119586 doi "https://doi.org/10.1109/cac57257.2022.10055033" @default.
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