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Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation
http://hdl.handle.net/10131/0002001194
http://hdl.handle.net/10131/00020011943e1b0a85-8dd6-4719-92d9-e16d6f97cf34
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
SNN-BOCDR_Manuscript_3.pdf (1.6 MB)
Download is available from 2025/2/14.
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2024-09-02 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Scattering, Real-time systems, Optical variables measurement, Signal processing, Optical fibers, Optical fiber sensors, Frequency measurement | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | 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 | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | 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 | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Data Acquisition Process, Maximum Search, Acquisition Process, Simulated Signals, Large Neural Networks, Maximum Allowable, Arbitrary Waveform Generator, Regression Neural Network | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Brillouin optical correlation-domain reflectometry (BOCDR), distributed optical fiber sensors, machine learning, neural networks (NNs), real-time measurement | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 著者 |
Yuguo, Yao
× Yuguo, Yao
ORCID
0000-0003-0061-7003
× Yuangang, Lu
ORCID
0000-0001-5977-0809
× Mizuno, Yosuke
ORCID
0000-0002-3362-4720
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | 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. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : IEEE Transactions on Instrumentation and Measurement 巻 72, 発行日 2023 |
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| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 00189456 | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA00667922 | |||||||||||
| DOI | ||||||||||||
| 関連タイプ | isVersionOf | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1109/TIM.2023.3244253 | |||||||||||
| 権利 | ||||||||||||
| 言語 | en | |||||||||||
| 権利情報Resource | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/#accepted | |||||||||||
| 権利情報 | © 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. | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | AM | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Institute of Electrical and Electronics Engineers (IEEE) | |||||||||||
| 言語 | en | |||||||||||
