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

作者：Shin Gi Wook; Juhn J W; Kwon G I; Son S H; Hahn S H^*

来源：Fusion Engineering and Design, 2018, 129: 341-344.

DOI：10.1016/j.fusengdes.2017.12.011

• KSTAR
• Plasma control
• Machine learning
• L-H transition
• Support vector machine

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.

• 出版日期2018-4