Kinetic equilibrium reconstruction for the NBI-and ICRH-heated H-mode plasma on EAST tokamak

The equilibrium reconstruction is important to study the tokamak plasma physical processes.To analyze the contribution of fast ions to the equilibrium,the kinetic equilibria at two time-slices in a typical H-mode discharge with different auxiliary heatings are reconstructed by using magnetic diagnostics,kinetic diagnostics and TRANSP code.It is found that the fast-ion pressure might be up to one-third of the plasma pressure and the contribution is mainly in the core plasma due to the neutral beam injection power is primarily deposited in the core region.The fast-ion current contributes mainly in the core region while contributes little to the pedestal current.A steep pressure gradient in the pedestal is observed which gives rise to a strong edge current.It is proved that the fast ion effects cannot be ignored and should be considered in the future study of EAST.

Shallow Feature-driven Dual-edges Localization Network for Weakly Supervised Localization

Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.