Abstract
In recent years, Electron Cyclotron Emission Imaging (ECEI) diagnostics and many other imaging diagnostics have become increasingly important in magnetic confinement fusion research. When the image quality becomes worse due to bad pixels, it becomes an important issue for imaging diagnostics. To automatically identify and classify abnormal ECEI signals, a classification algorithm for ECEI signals based on machine learning was developed for the J-TEXT ECEI diagnostic. Incorporated with the digital control function of J-TEXT ECEI, the channels of low-attenuation saturated signals and weak signals can be corrected by adjusting the attenuation levels. At present, the automatic data cleaning and feedback conditioning unit has been set up and applied to the J-TEXT ECEI. The accuracy rate of the classification algorithm on the external test dataset reaches 93.8%. Feedback conditioning can be completed between two discharge shots. This unit can preprocess the diagnostic data for physical analysis and improve the quality of ECEI signals.
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