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http://bura.brunel.ac.uk/handle/2438/26987
Title: | Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network |
Authors: | Wang, C Wang, Z Ma, L Dong, H Sheng, W |
Keywords: | adversarial learning;data augmentation;multi-head self-attention mechanism;pipeline fault diagnosis;subdomain alignment |
Issue Date: | 12-May-2023 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Wang, C. et al. (2023) 'Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network', IEEE Transactions on Industrial Informatics, 2023, 0 (early access), pp. 1 - 11. doi: 10.1109/TII.2023.3275701. |
Description: | This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/TII.2023.3275701, IEEE Transactions on Industrial Informatics. |
URI: | https://bura.brunel.ac.uk/handle/2438/26987 |
DOI: | https://doi.org/10.1109/TII.2023.3275701 |
ISSN: | 1551-3203 |
Other Identifiers: | ORCID iD: Chuang Wang https://orcid.org/0000-0001-8938-9312; Zidong Wang https://orcid.org/0000-0002-9576-7401; Lifeng Ma https://orcid.org/0000-0002-1839-6803; Hongli Dong https://orcid.org/0000-0001-8531-6757; Weiguo Sheng https://orcid.org/0000-0001-9680-5126. |
Appears in Collections: | Dept of Computer Science Research Papers |
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