Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning
| Autor: | Shi Jiulin, Shengpeng Wan, Juan Liu, Bin Liu, Rusheng Zhuo, Xingdao He, Qiang Wu, Wu Jinyi, Xinliang Xu, Jizhou Sun, Xiong Xinzhong |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Sagnac effect
Fiber (mathematics) business.industry Computer science F300 Deep learning 010401 analytical chemistry Function (mathematics) 01 natural sciences 0104 chemical sciences Interferometry Intrusion Position (vector) Electronic engineering Probability distribution Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| ISSN: | 1530-437X |
| Popis: | For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning applying to sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points. |
| Databáze: | OpenAIRE |
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