Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e.

relative self-dependent calibration of electron cyclotron emission imaging via shape matching.

Electron Cyclotron Emission Imaging (ECEI) is a diagnostic system which measures 2-D electron temperature profiles with high spatial-temporal resolution. Usually only the normalized electron temperature fluctuations are utilized to investigate the magnetohydrodynamics modes due to the difficulties of ECEI calibration. In this paper, we developed a self-dependent calibration method for 24 × 16 channel high-resolution ECEI on the Experimental Advanced Superconducting Tokamak.

Diagnostic capacity of electron cyclotron emission imaging system with continuous large observation area on EAST tokamak.

Electron cyclotron emission imaging on EAST provides direct measurements of the 2-D electron temperature dynamics in a continuous large observation area with high temporal and spatial resolution. Besides the normal MHD investigation, a system with a view field large enough to cover the core plasma region has been applied to extract more plasma information, such as the plasma center location, the deposition location of auxiliary heating, and the core toroidal rotation speed. These results solely based on electron cyclotron emission imaging (ECEI) data are consistent with the results of the equilibrium fitting (EFIT), numerical code, and other diagnostics, which indicate the powerful diagnostic capacity of this ECEI system.