{"title":"基于机器学习和偏振的光纤应变传感器系统开发","authors":"Yao Zhao, Weiwei Duan and Lili Yuan","doi":"10.35848/1347-4065/ad39bc","DOIUrl":null,"url":null,"abstract":"Based on the principle that the polarization state of light propagating in a single-mode fiber changes with external strains, an optical fiber sensor system based on machine learning and polarization for multi-point strain measurement is proposed. To address the influence of the front sensor on the rear sensor and to minimize interference from unrelated inputs, we have employed a data processing method that constructs an individual neural network model for each sensor. This approach uses the polarization state of the reflected light of the sensors as the neural networks’ input and the sensors’ rotation angles as the output, training the designed neural networks for learning. The trained neural networks produce predicted outputs that demonstrate high consistency with the experimental data, achieving an average prediction accuracy of 99% on test data. These results validate the effectiveness of our sensor system and data processing method.","PeriodicalId":14741,"journal":{"name":"Japanese Journal of Applied Physics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of optical fiber strain sensor system based on machine learning and polarization\",\"authors\":\"Yao Zhao, Weiwei Duan and Lili Yuan\",\"doi\":\"10.35848/1347-4065/ad39bc\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the principle that the polarization state of light propagating in a single-mode fiber changes with external strains, an optical fiber sensor system based on machine learning and polarization for multi-point strain measurement is proposed. To address the influence of the front sensor on the rear sensor and to minimize interference from unrelated inputs, we have employed a data processing method that constructs an individual neural network model for each sensor. This approach uses the polarization state of the reflected light of the sensors as the neural networks’ input and the sensors’ rotation angles as the output, training the designed neural networks for learning. The trained neural networks produce predicted outputs that demonstrate high consistency with the experimental data, achieving an average prediction accuracy of 99% on test data. These results validate the effectiveness of our sensor system and data processing method.\",\"PeriodicalId\":14741,\"journal\":{\"name\":\"Japanese Journal of Applied Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Japanese Journal of Applied Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.35848/1347-4065/ad39bc\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese Journal of Applied Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.35848/1347-4065/ad39bc","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
Development of optical fiber strain sensor system based on machine learning and polarization
Based on the principle that the polarization state of light propagating in a single-mode fiber changes with external strains, an optical fiber sensor system based on machine learning and polarization for multi-point strain measurement is proposed. To address the influence of the front sensor on the rear sensor and to minimize interference from unrelated inputs, we have employed a data processing method that constructs an individual neural network model for each sensor. This approach uses the polarization state of the reflected light of the sensors as the neural networks’ input and the sensors’ rotation angles as the output, training the designed neural networks for learning. The trained neural networks produce predicted outputs that demonstrate high consistency with the experimental data, achieving an average prediction accuracy of 99% on test data. These results validate the effectiveness of our sensor system and data processing method.
期刊介绍:
The Japanese Journal of Applied Physics (JJAP) is an international journal for the advancement and dissemination of knowledge in all fields of applied physics. JJAP is a sister journal of the Applied Physics Express (APEX) and is published by IOP Publishing Ltd on behalf of the Japan Society of Applied Physics (JSAP).
JJAP publishes articles that significantly contribute to the advancements in the applications of physical principles as well as in the understanding of physics in view of particular applications in mind. Subjects covered by JJAP include the following fields:
• Semiconductors, dielectrics, and organic materials
• Photonics, quantum electronics, optics, and spectroscopy
• Spintronics, superconductivity, and strongly correlated materials
• Device physics including quantum information processing
• Physics-based circuits and systems
• Nanoscale science and technology
• Crystal growth, surfaces, interfaces, thin films, and bulk materials
• Plasmas, applied atomic and molecular physics, and applied nuclear physics
• Device processing, fabrication and measurement technologies, and instrumentation
• Cross-disciplinary areas such as bioelectronics/photonics, biosensing, environmental/energy technologies, and MEMS