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A self-supervised temporal temperature prediction method based on dilated contrastive learning
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Lei, Yongxiang, Chen, Xiaofang, Xie, Yongfang and Cen, Lihui (2022) A self-supervised temporal temperature prediction method based on dilated contrastive learning. Journal of Process Control, 120 . pp. 150-158. doi:10.1016/j.jprocont.2022.11.005 ISSN 0959-1524.
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Official URL: http://dx.doi.org/10.1016/j.jprocont.2022.11.005
Abstract
Due to the scarcity of the labeled data, traditional supervised learning methods have a limited application scope, which caused the supervised-based model performance will greatly be decreased. In this paper, we propose a promising model based on self-supervised learning. To update the weight and the contrastive relation in the features, a new self-supervised loss, is introduced. First, the convolution neural network is used in the proposed network to extract the deep feature in the first processing. Second, the self-supervised long–short time memory (LSTM) sequential is constructed for further deal. At last, the teacher net and student net have coordinately fine-tuned the credibility of the temperature prediction. By the experimental comparison, our proposed CNN-SSDLSTM is competitive with other supervised and semi-supervised methods. The evaluation experiments achieve state-of-the-art performance in aluminum electrolysis temperature prediction applications.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Supervised learning (Machine learning), Materials at high temperatures | |||||||||
Journal or Publication Title: | Journal of Process Control | |||||||||
Publisher: | Elsevier Ltd. | |||||||||
ISSN: | 0959-1524 | |||||||||
Official Date: | December 2022 | |||||||||
Dates: |
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Volume: | 120 | |||||||||
Page Range: | pp. 150-158 | |||||||||
DOI: | 10.1016/j.jprocont.2022.11.005 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Copyright Holders: | Elsevier Ltd. | |||||||||
Date of first compliant deposit: | 13 January 2023 | |||||||||
Date of first compliant Open Access: | 28 November 2023 | |||||||||
RIOXX Funder/Project Grant: |
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