An improved deep learning-based algorithm for 3D reconstruction of vacuum arcs.

Extensive attempts have been made to enable the application of deep learning to 3D plasma reconstruction. However, due to the limitation on the number of available training samples, deep learning-based methods have insufficient generalization ability compared to the traditional iterative methods. This paper proposes an improved algorithm named convolutional neural network-maximum likelihood expectation maximization-split-Bergman (CNN-MLEM-SB) based on the combination of the deep learning CNN and an iterative algorithm known as MLEM-SB.

3D reconstruction of dynamic behaviors of vacuum arcs under transverse magnetic fields via computer tomography.

The transverse magnetic field (TMF) contacts make the vacuum arcs deviate from the axisymmetric structure, so complete spatiotemporal evolution information of the plasma cannot be obtained by adopting one- or two-dimensional (2D) diagnostic methods. To address the issues, computer tomography was introduced in this paper. First, a multi-angle diagnostic imaging system based on split fiber bundles was proposed, which used a high-speed camera to simultaneously acquire eight angles of the arc image over time.