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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26046
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dc.contributor.authorWang, C-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, W-
dc.contributor.authorShen, Y-
dc.contributor.authorDong, H-
dc.date.accessioned2023-03-03T16:10:43Z-
dc.date.available2023-03-03T16:10:43Z-
dc.date.issued2022-11-23-
dc.identifierORCID iDs: Zidong Wang https://orcid.org/0000-0002-9576-7401; Weibo Liu https://orcid.org/0000-0002-8169-3261; Yuxuan Shen https://orcid.org/0000-0003-4870-9038; Hongli Dong https://orcid.org/0000-0001-8531-6757.-
dc.identifier3503913-
dc.identifier.citationWang, C. et al. (2022) 'A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples', IEEE Transactions on Instrumentation and Measurement, 72, pp. 1 - 13. doi: 10.1109/TIM.2022.3220302.en_US
dc.identifier.issn0018-9456-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/26046-
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, U21A2019, 61873058 and 62103095); Hainan Province Science and Technology Special Fund of China (Grant Number: ZDYF2022SHFZ105); 10.13039/501100005046-Natural Science Foundation of Heilongjiang Province of China (Grant Number: LH2021F005); 10.13039/100005156-Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent1 - 13-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2022 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. See: https://www.ieee.org/publications/rights/rights-policies.html-
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html-
dc.subjectdeep transfer learning (DTL)en_US
dc.subjectdynamic thresholden_US
dc.subjectlong short-term memory networken_US
dc.subjectpipeline leakage detection (PLD)en_US
dc.subjectsmall samplesen_US
dc.titleA Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samplesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TIM.2022.3220302-
dc.relation.isPartOfIEEE Transactions on Instrumentation and Measurement-
pubs.publication-statusPublished-
pubs.volume72-
dc.identifier.eissn1557-9662-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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