An Accurate Data Cleaning Procedure for Electron Cyclotron Emission Imaging on EAST Tokamak Based on Methodology of Machine Learning

Abstract

A new data cleaning procedure for electron cyclotron emission imaging (ECEI) of EAST tokamak is constructed. Machine learning techniques, including SVM and Decision tree, are applied to identifying saturated, zero, and weak signals of ECEI raw data, which not only reduces the effort of researchers for data analysis, but also improves the accuracy of data preprocessing. To enhance the reliability of the procedure, proper training sets are sampled based on massive raw data from the experiments of ECEI on EAST tokamak. Window size of temporal signal, kernel function, and other model parameters are obtained after model training. Consequently, the recognition rates of saturated, zero, and weak signals in raw data are 99.4%, 99.86%, and 99.9%, respectively, which proves the accuracy of this procedure.