Research on vibration pattern recognition based on phase‐sensitive optical time domain reflectometry...
Research on vibration pattern recognition based on phase‐sensitive optical time domain reflectometry and voting fully convolution neural networks
About this item
Full title
Author / Creator
Liao, Yunhong , Li, Ke and Gong, Yandong
Publisher
John Wiley & Sons, Inc
Journal title
Language
English
Formats
Publication information
Publisher
John Wiley & Sons, Inc
Subjects
More information
Scope and Contents
Contents
A method that combines phase‐sensitive optical time domain reflectometry with deep learning to construct new voting fully convolution neural networks (VoteFCNs) is proposed. Compared to the traditional convolutional network, the VoteFCN can be input with data of random size and requires less parameters so that the training speed can be improved gre...
Alternative Titles
Full title
Research on vibration pattern recognition based on phase‐sensitive optical time domain reflectometry and voting fully convolution neural networks
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_f9282cd80fbd42e88306ad819fdbbd9c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f9282cd80fbd42e88306ad819fdbbd9c
Other Identifiers
ISSN
1751-8768
E-ISSN
1751-8776
DOI
10.1049/ote2.12116
