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Comparative analysis on the deployment of machine learning algorithms in the distributed brillouin optical time domain analysis (BOTDA) fiber sensor

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dc.citedby14
dc.contributor.authorNordin N.D.en_US
dc.contributor.authorZan M.S.D.en_US
dc.contributor.authorAbdullah F.en_US
dc.contributor.authorid57217851042en_US
dc.contributor.authorid24767242400en_US
dc.contributor.authorid56613644500en_US
dc.date.accessioned2023-05-29T08:06:53Z
dc.date.available2023-05-29T08:06:53Z
dc.date.issued2020
dc.description.abstractThis paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo79
dc.identifier.doi10.3390/PHOTONICS7040079
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85092575254
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092575254&doi=10.3390%2fPHOTONICS7040079&partnerID=40&md5=dfd9e430bfa8f213f569f9fba8f11f12
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25127
dc.identifier.volume7
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitlePhotonics
dc.titleComparative analysis on the deployment of machine learning algorithms in the distributed brillouin optical time domain analysis (BOTDA) fiber sensoren_US
dc.typeArticleen_US
dspace.entity.typePublication
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