Implementation of spectral clustering algorithm for automatic identification of plasma coherence patterns

Abstract

The number of data accumulated by controllable nuclear fusion devices is too large, and a large number of data have not been fully exploited. In such big data processing machine learning can play an important role. Therefore, in this work the spectral clustering method is used to realize the automatic processing of data, which can easily and quickly find the pattern information contained in the data. The discovery of these patterns is of great significance in improving plasma confinement and understanding plasma physics. In addition, in this work the spectral clustering method is applied to the electron cyclotron emission imaging (ECEI), one-dimensional diagnostic system electron cyclotron emissiometer, magnetic probe, soft X-ray, fast radiation (fast bolometer) and other different diagnostic systems on the EAST tokamak device. The sawtooth pattern is identified, the migration of the spectral clustering method is verified, and the problems of poor data processing migration in supervised learning and the need to rely on a large number of labeled data are solved. Finally, in this work, the ECEI and magnetic probe data are used to discover a possible new mode in the time domain and frequency domain respectively, which provides a new idea for exploring new modes.