Publication detail

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

DEJDAR, P. MYŠKA, V. MÜNSTER, P. BURGET, R.

Original Title

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

Type

conference paper

Language

English

Original 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

Authors

DEJDAR, P.; MYŠKA, V.; MÜNSTER, P.; BURGET, R.

Released

25. 5. 2021

Publisher

IEEE

ISBN

978-1-7281-5099-4

Book

2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) Proceedings

Pages from

1

Pages to

4

Pages count

4

URL

https://ieeexplore.ieee.org/document/9430469

Full text in the Digital Library

http://hdl.handle.net/11012/203013

BibTex

@inproceedings{BUT171700,
  author="Petr {Dejdar} and Vojtěch {Myška} and Petr {Münster} and Radim {Burget}",
  title="Trains Detection Using State of Polarization Changes Measurement and Convolutional Neural Networks",
  booktitle="2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) Proceedings",
  year="2021",
  pages="1--4",
  publisher="IEEE",
  doi="10.1109/INERTIAL51137.2021.9430469",
  isbn="978-1-7281-5099-4",
  url="https://ieeexplore.ieee.org/document/9430469"
}