{"title":"Deep learning based force recognition using the specklegrams from multimode fiber","authors":"Jie Lu, Han Gao, Yuanyuan Liu, H. Hu","doi":"10.1080/10739149.2023.2183406","DOIUrl":null,"url":null,"abstract":"Abstract The force induced variations of interferences in multimode fiber (MMF) are recognized by the output specklegrams. In this work, the classification of specklegrams is reported to identify the magnitude and position of the force applied on the MMF. The specklegrams from the MMF are recorded by a CCD camera at different force conditions. Because of the large number of transverse modes in the fiber, the specklegrams contains abundant information about the force applied on fiber states. By employing a convolutional neural network (CNN), the classification accuracies of the force position and magnitude on the fiber were 95.91% and 96.67% for test dataset. This reported scheme has the advantages of low cost and simple structure and is suitable to identify specific types of force in distributed sensing applications.","PeriodicalId":13547,"journal":{"name":"Instrumentation Science & Technology","volume":"51 1","pages":"610 - 620"},"PeriodicalIF":1.3000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Instrumentation Science & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10739149.2023.2183406","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract The force induced variations of interferences in multimode fiber (MMF) are recognized by the output specklegrams. In this work, the classification of specklegrams is reported to identify the magnitude and position of the force applied on the MMF. The specklegrams from the MMF are recorded by a CCD camera at different force conditions. Because of the large number of transverse modes in the fiber, the specklegrams contains abundant information about the force applied on fiber states. By employing a convolutional neural network (CNN), the classification accuracies of the force position and magnitude on the fiber were 95.91% and 96.67% for test dataset. This reported scheme has the advantages of low cost and simple structure and is suitable to identify specific types of force in distributed sensing applications.
期刊介绍：
Instrumentation Science & Technology is an internationally acclaimed forum for fast publication of critical, peer reviewed manuscripts dealing with innovative instrument design and applications in chemistry, physics biotechnology and environmental science. Particular attention is given to state-of-the-art developments and their rapid communication to the scientific community.
Emphasis is on modern instrumental concepts, though not exclusively, including detectors, sensors, data acquisition and processing, instrument control, chromatography, electrochemistry, spectroscopy of all types, electrophoresis, radiometry, relaxation methods, thermal analysis, physical property measurements, surface physics, membrane technology, microcomputer design, chip-based processes, and more.
Readership includes everyone who uses instrumental techniques to conduct their research and development. They are chemists (organic, inorganic, physical, analytical, nuclear, quality control) biochemists, biotechnologists, engineers, and physicists in all of the instrumental disciplines mentioned above, in both the laboratory and chemical production environments. The journal is an important resource of instrument design and applications data.
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