Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra-fast imaging

Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra-fast imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra-fast imaging Sarah Chouchene, Frédéric Brochard, Mikaël Desecures, Nicolas Lemoine, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4668895/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This study explores the application of machine learning techniques for detecting and tracking plasma filaments around the boundary of magnetically confined tokamak plasmas. Plasma filaments, also called blobs, are responsible for enhanced turbulent transport across magnetic field lines, and their accurate characterization is crucial for optimizing the performance of magnetic fusion devices. We present a novel approach that combines machine learning methods applied to data obtained from ultra-fast cameras, including YOLO (You Only Look Once) for object detection, semantic segmentation, and specific tracking methods. This approach enables fast and accurate detection and tracking of filaments while overcoming the limitations of conventional methods, which are time-consuming and prone to human subjectivity. A significant advance in our study lies in the development of a method for automatically labeling a large batch of data, which greatly facilitates the training of supervised machine learning algorithms. Using these techniques, we obtained promising results demonstrating a significant improvement over conventional tracking methods, achieving a detection accuracy of up to 98.8 %, while reducing the inference time per frame by 15% to 31% compared to conventional Kalman filter tracking. These results open up new perspectives for investigating turbulent phenomena in tokamaks, and could have important implications for the development of controlled nuclear fusion. Physical sciences/Physics/Plasma physics/Magnetically confined plasmas Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviews received at journal 13 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviewers invited by journal 08 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Editor invited by journal 03 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 01 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4668895","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330458871,"identity":"bb5636d2-6381-4899-9c75-29557d1f0ada","order_by":0,"name":"Sarah Chouchene","email":"data:image/png;base64,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","orcid":"","institution":"University of Lorraine","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Chouchene","suffix":""},{"id":330458872,"identity":"3d776950-49d8-4b2c-9004-48b006cd25ea","order_by":1,"name":"Frédéric Brochard","email":"","orcid":"","institution":"University of Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Frédéric","middleName":"","lastName":"Brochard","suffix":""},{"id":330458873,"identity":"64ea4ac4-d16f-419f-bf0d-844e6d06b473","order_by":2,"name":"Mikaël Desecures","email":"","orcid":"","institution":"Aprex solutions","correspondingAuthor":false,"prefix":"","firstName":"Mikaël","middleName":"","lastName":"Desecures","suffix":""},{"id":330458874,"identity":"ce61e20f-0dd9-4e07-a8b8-9352983926c9","order_by":3,"name":"Nicolas Lemoine","email":"","orcid":"","institution":"University of Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Lemoine","suffix":""},{"id":330458875,"identity":"1a3e687e-2a30-4f4f-a136-9a39f19bc974","order_by":4,"name":"Jordan Cavalier","email":"","orcid":"","institution":"Institute of Plasma Physics","correspondingAuthor":false,"prefix":"","firstName":"Jordan","middleName":"","lastName":"Cavalier","suffix":""}],"badges":[],"createdAt":"2024-07-01 14:51:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4668895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4668895/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79251-z","type":"published","date":"2024-11-14T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69286876,"identity":"1cfbdf46-ff07-4da0-8e91-b27cb5992a11","added_by":"auto","created_at":"2024-11-18 19:40:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1538501,"visible":true,"origin":"","legend":"","description":"","filename":"Applicationofmachinelearningfordetectingandtrackingturbulentstructuresinplasmafusiondevicesusingultrafastcamera1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4668895/v1_covered_a7b21c7e-0a09-4e22-879c-7c4a5fd31233.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra-fast imaging","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4668895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4668895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study explores the application of machine learning techniques for detecting and tracking plasma filaments around the boundary of magnetically confined tokamak plasmas. 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