Tokamak visible image sequence recognition using nonlocal spatio-temporal CNN for attention needed area localization

Abstract

In this paper, we report a study that was conducted to explore the feasibility of developing a system that classifies the image sequence as ‘disruptive’ shot images or ‘non-disruptive’ shot images. The classifier identifies an image sequence as ‘non-disruptive’ shots or ‘disruptive’ shots using a non-local spatio-temporal 3D convolution neural network (CNN) from image sequences from the Tokamak visible imaging diagnostic system. To analyze the classification result, we localize an area that has contributed to the classification of an image sequence. We use class activation mapping (CAM) for CNN with global average pooling to localize the area. To train this classifier, we created a plasma disruption image sequence dataset using the data acquired from the KSTAR experiment. This classifier recognized disruption image sequences on the test dataset with 91.11% accuracy. Analysis of the CAMs of these image sequences revealed that this classifier recognizes the disruption of the plasma with a relative change in brightness over time in areas other than the plasma area of the image. Through this work, we will be able to develop a system that automatically classifies plasma disruption image sequence after experiments.