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 language | English |
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Title of host publication | 2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017 |
Publisher | IEEE |
Pages | 1-4 |
Number of pages | 4 |
Volume | 2017-January |
ISBN (Electronic) | 9781509062904 |
DOIs | |
Publication status | Published - 22 Nov 2017 |
Event | 2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017 - Singapore, Singapore Duration: 31 Jul 2017 → 4 Aug 2017 |
Conference
Conference | 2017 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2017 |
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Country/Territory | Singapore |
City | Singapore |
Period | 31/07/17 → 4/08/17 |
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