Real-time classification of L-H transition and ELM in KSTAR-Reference-Cited by-同舟云学术

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Real-time classification of L-H transition and ELM in KSTAR

Published:2020-08 Issue: Volume:157 Page:111634
ISSN:0920-3796
Container-title:Fusion Engineering and Design
language:en
Short-container-title:Fusion Engineering and Design

Author:

Shin Giwook,Juhn J.-W.^ORCID,Kwon G.I.^ORCID,Hahn S.-H.^ORCID

Funder

Korean Ministry of Science and ICT under NFRI R&D programs

NST-NFRI Postdoctoral Research Fellowship for Young Scientists at NFRI in south KOREA

Publisher

Elsevier BV

Subject

Mechanical Engineering,General Materials Science,Nuclear Energy and Engineering,Civil and Structural Engineering

Reference17 articles.

1. The physics of edge localized modes (ELMs) and their role in power and particle exhaust;Zohm;Plasma Phys. Control. Fusion.,1996

2. Edge transport barriers in magnetic fusion plasmas;Gohil;C. R. Physique,2006

3. Automatic detection of L-H transition in KSTAR by support vector machine;Shin;Fusion Eng. Des.,2018

4. Statistical Learning Theory;Vapnik,1998

5. Design and construction of the KSTAR tokamak;Lee;Nucl. Fusion,2001

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identifying L-H transition in HL-2A through deep learning;Plasma Physics and Controlled Fusion;2024-09-11

2. Overview of the KSTAR experiments toward fusion reactor;Nuclear Fusion;2024-08-23

3. Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements;Machine Learning: Science and Technology;2024-07-16

4. Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence;Nuclear Fusion;2024-05-14

5. Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR;Nuclear Fusion;2023-07-18

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