Residual-aware deep attention graph convolutional network via unveiling data latent interactions for product quality prediction in industrial processes

Author:

Chen YitaoORCID,Wang Yalin,Sui Qingkai,Yuan Xiaofeng,Wang Kai,Liu ChenliangORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference38 articles.

1. Hyperspectral image classification based on deep attention graph convolutional network;Bai;IEEE Transactions on Geoscience and Remote Sensing,2021

2. Cai, X., Huang, C., Xia, L., Ren, X., 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191.

3. Variational progressive-transfer network for soft sensing of multirate industrial processes;Chai;IEEE Transactions on Cybernetics,2021

4. Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes;Chen;Information Sciences,2022

5. Knowledge automation through graph mining, convolution, and explanation framework: A soft sensor practice;Chen;IEEE Transactions on Industrial Informatics,2021

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