Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis
To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-04-01
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Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/8/2724 |
Summary: | To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential. |
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ISSN: | 1424-8220 |