Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network

Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neura...

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Main Authors: F. O. Barino, F. S. Delgado, A. Bessa dos Santos
Format: Article
Language:English
Published: Sociedade Brasileira de Microondas e Optoeletrônica; Sociedade Brasileira de Eletromagnetismo 2020-03-01
Series:Journal of Microwaves, Optoelectronics and Electromagnetic Applications
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742020000100001&tlng=en
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author F. O. Barino
F. S. Delgado
A. Bessa dos Santos
author_facet F. O. Barino
F. S. Delgado
A. Bessa dos Santos
author_sort F. O. Barino
collection DOAJ
description Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neural network (ANN). Furthermore, we evaluate and compare the predictive performance of the interrogation model considering the square correlation coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the ANN model could be successfully employed to estimate the load intensity and azimuthal angle using a single LPG sensor.
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publishDate 2020-03-01
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spelling doaj.art-7e9de0efcb4f42b0bc6f941f0223900d2022-12-22T04:16:28ZengSociedade Brasileira de Microondas e Optoeletrônica; Sociedade Brasileira de EletromagnetismoJournal of Microwaves, Optoelectronics and Electromagnetic Applications2179-10742020-03-0119111010.1590/2179-10742020v19i11809Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural NetworkF. O. Barinohttps://orcid.org/0000-0003-3433-0887F. S. Delgadohttps://orcid.org/0000-0002-9779-645XA. Bessa dos Santoshttps://orcid.org/0000-0003-2742-7664Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neural network (ANN). Furthermore, we evaluate and compare the predictive performance of the interrogation model considering the square correlation coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the ANN model could be successfully employed to estimate the load intensity and azimuthal angle using a single LPG sensor.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742020000100001&tlng=enArtificial neural networklong period gratingoptical fiber sensortransverse load
spellingShingle F. O. Barino
F. S. Delgado
A. Bessa dos Santos
Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
Journal of Microwaves, Optoelectronics and Electromagnetic Applications
Artificial neural network
long period grating
optical fiber sensor
transverse load
title Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
title_full Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
title_fullStr Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
title_full_unstemmed Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
title_short Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
title_sort transverse load discrimination in long period fiber grating via artificial neural network
topic Artificial neural network
long period grating
optical fiber sensor
transverse load
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742020000100001&tlng=en
work_keys_str_mv AT fobarino transverseloaddiscriminationinlongperiodfibergratingviaartificialneuralnetwork
AT fsdelgado transverseloaddiscriminationinlongperiodfibergratingviaartificialneuralnetwork
AT abessadossantos transverseloaddiscriminationinlongperiodfibergratingviaartificialneuralnetwork