Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91278
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dc.contributorPhotonics Research Centre-
dc.contributorDepartment of Electrical Engineering-
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorCui, J-
dc.creatorLuo, H-
dc.creatorLu, J-
dc.creatorCheng, X-
dc.creatorTam, HY-
dc.date.accessioned2021-11-02T08:21:56Z-
dc.date.available2021-11-02T08:21:56Z-
dc.identifier.urihttp://hdl.handle.net/10397/91278-
dc.language.isoenen_US
dc.publisherOptical Society of Americaen_US
dc.rights© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (https://www.osapublishing.org/library/license_v1.cfm#VOR-OA)en_US
dc.rightsJournal © 2021en_US
dc.rights© 2021 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.en_US
dc.rightsThe following publication Cui, J., Luo, H., Lu, J., Cheng, X., & Tam, H. Y. (2021). Random forest assisted vector displacement sensor based on a multicore fiber. Optics Express, 29(10), 15852-15864 is available at https://doi.org/10.1364/OE.425842en_US
dc.titleRandom forest assisted vector displacement sensor based on a multicore fiberen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage15852-
dc.identifier.epage15864-
dc.identifier.volume29-
dc.identifier.issue10-
dc.identifier.doi10.1364/OE.425842-
dcterms.abstractWe proposed a two-dimensional vector displacement sensor with the capability of distinguishing the direction and amplitude of the displacement simultaneously, with improved performance assisted by random forest, a powerful machine learning algorithm. The sensor was designed based on a seven-core multi-core fiber inscribed with Bragg gratings, with a displacement direction range of 0-360° and the amplitude range related to the length of the sensor body. The displacement information was obtained under a random circumstance, where the performances with theoretical model and random forest model were studied. With the theoretical model, the sensor performed well over a shorter linear range (from 0 to 9 mm). Whereas the sensor assisted with random forest algorithm exhibits better performance in two aspects, a wider measurement range (from 0 to 45 mm) and a reduced measurement error of displacement. Mean absolute errors of direction and amplitude reconstruction were decreased by 60% and 98%, respectively. The proposed displacement sensor shows the possibility of machine learning methods to be applied in point-based optical systems for multi-parameter sensing.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationOptics express, 10 May 2021, v. 29, no. 10, p. 15852-15864-
dcterms.isPartOfOptics express-
dcterms.issued2021-05-
dc.identifier.scopus2-s2.0-85105631807-
dc.identifier.pmid33985277-
dc.identifier.eissn1094-4087-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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