Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Nuclear Fusion

DOI: 10.1088/1741-4326/ad8bdb

Authors: Jiahao Zhang, Jun Zhao, Linzi Liu, Ruihai Tong, Wulyu Zhong, Yi Luo

Institutions: Sichuan University of Science and Engineering, Southwestern Institute of Physics

Language: English

Topic: Magnetic confinement fusion research

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,470

Funder: National Key Research and Development Program of China