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Robust impurity detection and tracking for tokamaks

Author
Abstract

A robust impurity detection and tracking code, able to generate large sets of dust tracks from tokamak camera footage, is presented. This machine learning–based code is tested with cameras from the Joint European Torus, Doublet-III-D, and Magnum-PSI and is able to generate dust tracks with a 65–100% classification accuracy. Moreover, the number dust particles detected from a single camera shot can be up to the order of 1000. Several areas of improvement for the code are highlighted, such as generating more significant training data sets and accounting for selection biases. Although the code is tested with dust in single two-dimensional camera views, it could easily be applied to multiple-camera stereoscopic reconstruction or nondust impurities.

Year of Publication
2020
Journal
Physical Review E
Volume
102
Issue
4
Number of Pages
043311
Date Published
10/2020
Publisher
American Physical Society
URL
https://pure.tue.nl/ws/files/164229006/PhysRevE.102.043311.pdf
DOI
10.1103/PhysRevE.102.043311
PId
5ea4950f4a1002643f2f0a19118d0466
Alternate Journal
Phys. Rev. E
Label
OA
Journal Article
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