Extraction of temperature distribution using deep neural networks for BOTDA sensing system

Biwei Wang, Nan Guo, Faisal Nadeem Khan, Abul Kalam Azad, Liang Wang, Changyuan Yu, Chao Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Extraction of temperature distribution using the method of deep neural networks (DNN) for Brillouin optical time domain analyzer (BOTDA) system is demonstrated experimentally. After appropriate training of DNN model, temperature distribution information along the fiber under test could be directly extracted from the experimentally obtained local Brillouin gain spectrums (BGS) using DNN without the need of calculating Brillouin frequency shift (BFS) and transforming it to temperature as conventional Lorentz curve fitting (LCF) method does. The results of Temperature extraction using DNN show comparable accuracy to that of using conventional LCF method.
Original languageEnglish
Title of host publication2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017
PublisherIEEE
Pages1-4
Number of pages4
Volume2017-January
ISBN (Electronic)9781509062904
DOIs
Publication statusPublished - 22 Nov 2017
Event2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017 - Singapore, Singapore
Duration: 31 Jul 20174 Aug 2017

Conference

Conference2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017
CountrySingapore
CitySingapore
Period31/07/174/08/17

Keywords

  • Brillouin Optical Time Domain Analyzer (BOTDA)
  • Deep Neural Networks (DNN)
  • Temperature Extraction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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