Research on vibration pattern recognition based on phase‐sensitive optical time domain reflectometry and voting fully convolution neural networks

Autor: Yunhong Liao, Ke Li, Yandong Gong
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Optoelectronics, Vol 18, Iss 3, Pp 63-69 (2024)
Druh dokumentu: article
ISSN: 1751-8776
1751-8768
DOI: 10.1049/ote2.12116
Popis: Abstract 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 greatly. The recognition results can be more accurate and more reliable if we use classification voting count and average recognition rate as the criteria to judge network training quality. At last, the training and identification were conducted by simulating such several disturbance events: walking, raining, climbing fence, hammering the ground optical fibre and normal outdoor environments. The results show that the average test accuracy of this method is about 93.4%.
Databáze: Directory of Open Access Journals
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