{"created":"2024-09-02T07:56:52.494633+00:00","id":2001194,"links":{},"metadata":{"_buckets":{"deposit":"2f02d817-825c-4dfb-b805-471442b3a8ff"},"_deposit":{"created_by":17,"id":"2001194","owner":"17","owners":[17],"pid":{"revision_id":0,"type":"depid","value":"2001194"},"status":"published"},"_oai":{"id":"oai:ynu.repo.nii.ac.jp:02001194","sets":["495:496"]},"author_link":[],"item_2_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2023","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"72","bibliographic_titles":[{"bibliographic_title":"IEEE Transactions on Instrumentation and Measurement","bibliographic_titleLang":"en"}]}]},"item_2_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In this article, a fiber-optic sensor based on Brillouin optical correlation-domain reflectometry (BOCDR) with a high repetition rate and real-time signal processing function is demonstrated with the assistance of swallow neural networks (NNs) constituted of no more than three hidden layers. In the proposed scheme, the signal processing time for every single Brillouin spectrum is compressed to less than the acquisition period of the spectrum by designing the structure of the NNs, on the basis of the knowledge of the relationship between the implementation time and the preliminary calculation count involved in the NNs. In the experiments with both simulated data and the real-world system, the performances of NNs with different sizes are studied from the perspectives of timing and accuracy. By training NN models with the data acquired from the experiments, real-time dynamic strain measurement is realized with a repetition rate of up to 20 kHz and a dynamic range of 4000με . Different from other works regarding machine learning-empowered measurement acceleration in distributed optical fiber sensors with an offline signal processing phase, the method proposed in this article enables consecutive monitoring of the parameters under test.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_2_publisher_35":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Institute of Electrical and Electronics Engineers (IEEE)","subitem_publisher_language":"en"}]},"item_2_relation_13":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1109/TIM.2023.3244253","subitem_relation_type_select":"DOI"}}]},"item_2_rights_14":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.","subitem_rights_language":"en","subitem_rights_resource":"https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/#accepted"}]},"item_2_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA00667922","subitem_source_identifier_type":"NCID"}]},"item_2_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"00189456","subitem_source_identifier_type":"PISSN"}]},"item_2_version_type_18":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"School of Electrical Engineering and Automation, Changshu Institute of Technology","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Yuguo, Yao","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"0000-0003-0061-7003","nameIdentifierScheme":"ORCID","nameIdentifierURI":"https://orcid.org/0000-0003-0061-7003"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Key Laboratory of Space Photoelectric Detection and Perception of Ministry of Industry and Information Technology, College of Astronautics","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Yuangang, Lu","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5977-0809","nameIdentifierScheme":"ORCID","nameIdentifierURI":"https://orcid.org/0000-0001-5977-0809"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Faculty of Engineering, Yokohama National University","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Mizuno, Yosuke","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"0000-0002-3362-4720","nameIdentifierScheme":"ORCID","nameIdentifierURI":"https://orcid.org/0000-0002-3362-4720"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2025-02-14"}],"fileDate":[{"fileDateType":"Issued","fileDateValue":"2023-02-13"}],"filename":"SNN-BOCDR_Manuscript_3.pdf","filesize":[{"value":"1.6 MB"}],"format":"application/pdf","url":{"objectType":"fulltext","url":"https://ynu.repo.nii.ac.jp/record/2001194/files/SNN-BOCDR_Manuscript_3.pdf"},"version_id":"0a50d2ee-837a-4c94-80a5-afef5edbe46e"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Scattering, Real-time systems, Optical variables measurement, Signal processing, Optical fibers, Optical fiber sensors, Frequency measurement","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Neural Network, Signal Processing, Artificial Neural Network, Dynamic Range, Optical Fiber, Hidden Layer, Repetition Rate, Optical Sensors, Real-time Measurements, Neural Network Training, Real-world Systems","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Performance Of Neural Networks, Acquisition Period, Dynamic Strain, Fiber Sensor, Data Processing, Root Mean Square Error, Training Dataset, High Speed, Background Noise, Real-time Conditions, Medium Size","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Data Acquisition Process, Maximum Search, Acquisition Process, Simulated Signals, Large Neural Networks, Maximum Allowable, Arbitrary Waveform Generator, Regression Neural Network","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Brillouin optical correlation-domain reflectometry (BOCDR), distributed optical fiber sensors, machine learning, neural networks (NNs), real-time measurement","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation","subitem_title_language":"en"}]},"item_type_id":"2","owner":"17","path":["496"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-09-02"},"publish_date":"2024-09-02","publish_status":"0","recid":"2001194","relation_version_is_last":true,"title":["Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation"],"weko_creator_id":"17","weko_shared_id":-1},"updated":"2024-09-02T08:01:07.541338+00:00"}