Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing re...

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Autores principales:	Fen Xiao, Mingxing Lv, Xinwan Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	neural networks Principal Components Analysis distributed fiber sensing Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/01702cf665bc40b7b11f2fd5e65c84f6
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id	oai:doaj.org-article:01702cf665bc40b7b11f2fd5e65c84f6
record_format	dspace
spelling	oai:doaj.org-article:01702cf665bc40b7b11f2fd5e65c84f62021-11-25T18:43:06ZFast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network10.3390/photonics81104742304-6732https://doaj.org/article/01702cf665bc40b7b11f2fd5e65c84f62021-10-01T00:00:00Zhttps://www.mdpi.com/2304-6732/8/11/474https://doaj.org/toc/2304-6732Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.Fen XiaoMingxing LvXinwan LiMDPI AGarticleneural networksPrincipal Components Analysisdistributed fiber sensingApplied optics. PhotonicsTA1501-1820ENPhotonics, Vol 8, Iss 474, p 474 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	neural networks Principal Components Analysis distributed fiber sensing Applied optics. Photonics TA1501-1820
spellingShingle	neural networks Principal Components Analysis distributed fiber sensing Applied optics. Photonics TA1501-1820 Fen Xiao Mingxing Lv Xinwan Li Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
description	Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.
format	article
author	Fen Xiao Mingxing Lv Xinwan Li
author_facet	Fen Xiao Mingxing Lv Xinwan Li
author_sort	Fen Xiao
title	Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
title_short	Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
title_full	Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
title_fullStr	Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
title_full_unstemmed	Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network
title_sort	fast measurement of brillouin frequency shift in optical fiber based on a novel feedforward neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/01702cf665bc40b7b11f2fd5e65c84f6
work_keys_str_mv	AT fenxiao fastmeasurementofbrillouinfrequencyshiftinopticalfiberbasedonanovelfeedforwardneuralnetwork AT mingxinglv fastmeasurementofbrillouinfrequencyshiftinopticalfiberbasedonanovelfeedforwardneuralnetwork AT xinwanli fastmeasurementofbrillouinfrequencyshiftinopticalfiberbasedonanovelfeedforwardneuralnetwork
_version_	1718410752588513280

Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

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