Automatic detection of L-H transition in KSTAR by support vector machine-Reference-Cited by-同舟云学术

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Automatic detection of L-H transition in KSTAR by support vector machine

Published:2018-04 Issue: Volume:129 Page:341-344
ISSN:0920-3796
Container-title:Fusion Engineering and Design
language:en
Short-container-title:Fusion Engineering and Design

Author:

Shin Gi Wook,Juhn J.-W.,Kwon G.I.,Son S.H.,Hahn S.H.

Funder

Ministry of Science and ICT in Korea

Publisher

Elsevier BV

Subject

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

Reference10 articles.

1. The KSTAR project: An advanced steady state superconducting tokamak experiment

2. Regime of Improved Confinement and High Beta in Neutral-Beam-Heated Divertor Discharges of the ASDEX Tokamak

3. Characteristics of the first H-mode discharges in KSTAR

4. Statistical Learning Thoery;Vapnik,1998

5. Automated estimation of L/H transition times at JET by combining Bayesian statistics and support vector machines

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1. 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

2. Preemptive RMP-driven ELM crash suppression automated by a real-time machine-learning classifier in KSTAR;Nuclear Fusion;2022-01-05

3. Nuclear Fusion Pattern Recognition by Ensemble Learning;Complexity;2021-06-29

4. Real-time classification of L-H transition and ELM in KSTAR;Fusion Engineering and Design;2020-08

5. A spectral clustering based method for automatic plasma pattern identification in electron cyclotron emission imaging on EAST Tokamak;Journal of Instrumentation;2020-07-06

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