Convolutional neural networks for solving problems of signal processing from segmental distributed fiber optic measuring networks

The work is devoted to the problem of critical situations recognition in real-time and localization of increased acoustic vibration in critical objects, based on the signals from segmental distributed fiber-optic measuring networks (DFOMN). To solve the problem, an architecture is proposed, and a mo...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1864 (1), p.12136
Main Authors: Kulchin, Yu. N., Kim, A.Yu
Format: Article
Language:English
Subjects:
Artificial neural networks
Fiber optics
Neural networks
Object recognition
Optical communication
Physics
Problem solving
Signal processing
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The work is devoted to the problem of critical situations recognition in real-time and localization of increased acoustic vibration in critical objects, based on the signals from segmental distributed fiber-optic measuring networks (DFOMN). To solve the problem, an architecture is proposed, and a model of a situational approach to using the Convolutional Neural Network as an effective classification method for processing large data arrays of the DFOMN is developed.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1864/1/012136