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Trains Detection Using State of Polarization Changes Measurement and Convolutional Neural Networks

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Date
2021-05-25
Author
Dejdar, Petr
Myška, Vojtěch
Münster, Petr
Burget, Radim
Altmetrics
10.1109/INERTIAL51137.2021.9430469
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Abstract
Fiber optic infrastructure security is of growing interest. The current distributed sensor systems are robust and expensive solutions, and their practical applications are uncommon. Research into simple and cost-effective solutions based on changes in the state of polarization is crucial. This paper expands the use of a vibration sensor based on the sensing of rapid changes in the state of polarization (SOP) of light in a standard single-mode optical fiber by using a convolutional neural network to detect trains running along the optical fiber infrastructure. It is a simple system that determines ongoing events near the optical fiber route by simply determining the signal boundaries that define the idle state. By using a neural network, it is possible to eliminate the distortion caused by the temperature changes and, for example, to improve detection in the the zones where the vibrations are not strong enough for a simple threshold resolution.
Keywords
artificial intelligence, machine learning, optical fiber sensor, state of polarization changes, vibration
Persistent identifier
http://hdl.handle.net/11012/203013
Document type
Peer reviewed
Document version
Postprint
Source
2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) Proceedings. 2021, p. 1-4.
https://ieeexplore.ieee.org/document/9430469
DOI
10.1109/INERTIAL51137.2021.9430469
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