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DC Field | Value | Language |
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dc.contributor.author | Wang, C | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Ma, L | - |
dc.contributor.author | Dong, H | - |
dc.contributor.author | Sheng, W | - |
dc.date.accessioned | 2023-08-18T10:51:57Z | - |
dc.date.available | 2023-05-12 | - |
dc.date.available | 2023-08-18T10:51:57Z | - |
dc.date.issued | 2023-05-12 | - |
dc.identifier | 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. | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26987 | - |
dc.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. | - |
dc.description.sponsorship | National Natural Science Foundation of China (Grant Number: 61933007, U21A2019 and 62273180); Hainan Province Science and Technology Special Fund of China (Grant Number: ZDYF2022SHFZ105); “Pioneer” and “Leading Goose” R&D Program of Zhejiang Province of China (Grant Number: 2023C01022). | en_US |
dc.format.extent | 1 - 11 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.subject | adversarial learning | en_US |
dc.subject | data augmentation | en_US |
dc.subject | multi-head self-attention mechanism | en_US |
dc.subject | pipeline fault diagnosis | en_US |
dc.subject | subdomain alignment | en_US |
dc.title | Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TII.2023.3275701 | - |
dc.relation.isPartOf | IEEE Transactions on Industrial Informatics | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1941-0050 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | 2.69 MB | Adobe PDF | View/Open |
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