Lossy compression of infrared videos in WEST (W Environment in Steady-state Tokamak).

During the 2019 C4 experimental campaign of the WEST (W Environment in Steady-state Tokamak) (France), the infrared diagnostic produced more than seven terabytes of uncompressed video data. Constraints on the computer infrastructure required for storage, backup, and especially offline access to infrared videos made the use of a compression algorithm mandatory. This paper proposes an innovative method to compress infrared videos with controlled temperature precision. This compression method is based on a controlled averaging of the video that maximizes the compression potential of standard lossless video codecs such as H264/AVC or HEVC. The combination of the loss introduction algorithm and the H264/AVC lossless video codec obtains the best compression ratio in the range of 8 to 41 with a maximum temperature error of 2 °C. This method also outperforms the JPEG-LS algorithm in terms of compression ratio and image quality for the same temperature precision.

Anomaly classification by inserting prior knowledge into a max-tree based method for divertor hot spot characterization on WEST tokamak.

The divertor of WEST (W Environment in Steady-state Tokamak) is the main component for plasma control and exhaust. It receives high heat fluxes, which can cause damage to plasma facing units above the allowable heat flux. Improving the operation safety on the actively cooled tungsten divertor is being researched in place at WEST, toward providing divertor monitoring solution for ITER. Divertor operation safety relies on detecting, monitoring, and classifying all hot spots on the divertor surface using infrared (IR) cameras. In this paper, a method based on max-tree representation and attributes of IR images is used to classify normal from abnormal strikelines on the divertor. The proposed method requires only high-level prior knowledge of abnormal temperatures and divertor structure but does not require any labeled data, unlike existing methods, such as support vector machines (SVMs) or convolutional neural networks (CNNs). The max-tree classifier method is tested on real IR images from the WEST tokamak and shows that 88% of hot spots are accurately classified with a small enough calculation duration that can be performed between two pulses.