Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80791
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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorCao, ZY-
dc.creatorGuo, N-
dc.creatorLi, MH-
dc.creatorYu, KL-
dc.creatorGao, KQ-
dc.date.accessioned2019-05-28T01:09:25Z-
dc.date.available2019-05-28T01:09:25Z-
dc.identifier.urihttp://hdl.handle.net/10397/80791-
dc.language.isoenen_US
dc.publisherOptical Society of Americaen_US
dc.rights© 2019 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.rights© 2019 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.rightsJournal © 2019en_US
dc.rightsThe following publication Zhiyuan Cao, Nan Guo, Meihong Li, Kuanglu Yu, and Kaiqiang Gao, "Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors," Opt. Express 27, 4549-4561 (2019) is available at https://dx.doi.org/10.1364/OE.27.004549en_US
dc.titleBack propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage4549en_US
dc.identifier.epage4561en_US
dc.identifier.volume27en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1364/OE.27.004549en_US
dcterms.abstractThis manuscript proposes a method based on back propagation (BP) neural network and the spectral subtraction method to quickly obtain sensing information in Brillouin fiber optics sensors. BP neural network's characteristics which can realize any complex nonlinear mapping help to determine the frequency shift section(s) information. The training function, transfer function and number of hidden layer nodes of BP neural network are determined with experimental data. The experimental results show that comparing with traditional Lorentz fitting algorithm and edge detection with Sobel operator, the BP neural network is about 1/12 in terms of time complexity with the Lorentz algorithm, about 1/9 with the edge detection based on Sobel operator; while the respective accuracy on determine the frequency shifted section(s) has improved by 79.4% and 27.9%.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationOptics express, 18 Feb. 2019, v. 27, no. 4, p. 4549-4561en_US
dcterms.isPartOfOptics expressen_US
dcterms.issued2019-
dc.identifier.isiWOS:000459152800078-
dc.identifier.pmid30876072-
dc.identifier.eissn1094-4087en_US
dc.description.validate201905 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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