A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26046

Title:	A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples
Authors:	Wang, C Wang, Z Liu, W Shen, Y Dong, H
Keywords:	deep transfer learning (DTL);dynamic threshold;long short-term memory network;pipeline leakage detection (PLD);small samples
Issue Date:	23-Nov-2022
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Citation:	Wang, 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.
URI:	https://bura.brunel.ac.uk/handle/2438/26046
DOI:	https://doi.org/10.1109/TIM.2022.3220302
ISSN:	0018-9456
Other Identifiers:	ORCID 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. 3503913
Appears in Collections:	Dept of Computer Science Research Papers

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