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

被引：6
|
作者：
Shin, Gi Wook [1 ]
Juhn, J. -W. [2 ]
Kwon, G. I. [2 ]
Son, S. H. [2 ]
Hahn, S. H. [1 ,2 ]
机构：
[1] Korea Univ Sci & Technol, 217 Gajeong Ro, Daejeon 34113, South Korea
[2] Natl Fus Res Inst, 113 Gwahak Ro, Daejeon 34133, South Korea
来源：
FUSION ENGINEERING AND DESIGN | 2018年 / 129卷
关键词：
KSTAR; Plasma control; Machine learning; L-H transition; Support vector machine; TOKAMAK;
D O I：
10.1016/j.fusengdes.2017.12.011
中图分类号：
TL [原子能技术]; O571 [原子核物理学];
学科分类号：
0827 ; 082701 ;
摘要：
Method for automatic detection of L-H transition using Support Vector Machine (SVM), a popular tool of supervised machine learning tools, has been evaluated in order to improve plasma density control in KSTAR. Through the SVM, a nonlinear classifier is trained to distinguish L-mode and H-mode using two kinds of diagnostic data measured in KSTAR. The trained classifier has been analyzed for possible usage on the real-time detection through the truncation of the training samples. Study on the optimization of the training samples, and corresponding accuracy change is made for evaluating feasibility for real-time implementations.
引用
收藏
页码：341 / 344
页数：4
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