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发表刊物：Journal of Instrumentation

关键字：Data Cleaning, Machine Learning ECEI, Tokamak

摘要：A new data cleaning procedure for the electron cyclotron emission imaging (ECEI) of the EAST tokamak is developed. Machine learning techniques, including support vector machine (SVM) and Decision Trees, are applied to the identification of saturated, zero, and weak signals of the ECEI raw data. As a result, the burden of data analysis is reduced, and the classification accuracy is improved. Proper training sets are sampled using the massive raw ECEI data from the EAST tokamak. The optimal window size of temporal signals, the kernel function, and other model parameters are obtained by the model training. Five-fold cross-validation (CV) is applied during modeling and an external testing set is employed to validate the prediction performance of models. The average recall rates on CV sets of saturated, zero, and weak signals are 95.9%, 96.72%, and 100%, respectively, which prove the accuracy of this procedure. Random Forest, as a comparative method, is also employed to deal with the same data sets. The average recall rates on CV sets of saturated, zero, and weak signals performed by Random Forest are 95.9%, 96.72%, and 95.88%. Our method has been proved to outperform Random Forest with small data sets.

合写作者：C Li,Ting Lan,Yulei Wang,Jian Liu,Jinlin Xie,Tao Lan,Hong LI,Hong Qin

学科门类：理学

卷号：13

页面范围：:P10029

是否译文：否

发表时间：2018-10-24

收录刊物：SCI

发布期刊链接：https://iopscience.iop.org/article/10.1088/1748-0221/13/10/P10029