Experimental identification of ion cyclotron emission on HL-2A using YOLO neural network algorithm

Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron Emission (ICE) is a potential fast ion diagnostic method in burning plasmas. To assess ICE as a fast ion diagnostic fo...

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Bibliographic Details
Main Authors: Jiahao Zhang, Jun Zhao, Linzi Liu, Ruihai Tong, Wulyu Zhong, Yi Luo
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Nuclear Fusion
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Online Access:https://doi.org/10.1088/1741-4326/ad8bdb
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Summary:Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron Emission (ICE) is a potential fast ion diagnostic method in burning plasmas. To assess ICE as a fast ion diagnostic for International Thermonuclear Experimental Reactor, real-time identification of ICE is required in the fast ion diagnostic flow. In the present work, we employed YOLO (You Only Look Once) to identify core and edge ICE in a large labeled database of HL-2A discharges, achieving a precision of 85.4% and a recall rate of 77.3%. Subsequent improvements to the YOLO model resulted in a noteworthy 8.3% increment in the recall rate. The developed identification method demonstrates significant potential for real-time application in identifying MHD instabilities.
ISSN:0029-5515