A Thomson scattering diagnostic system is under construction at the Joint Texas Experimental Tokamak（J-TEXT）. A 1064 nm Nd：YAG laser with 50 Hz repetition rate is used as the laser source. We have used a software f...A Thomson scattering diagnostic system is under construction at the Joint Texas Experimental Tokamak（J-TEXT）. A 1064 nm Nd：YAG laser with 50 Hz repetition rate is used as the laser source. We have used a software for careful and precise control of the laser through serial communication. A time sequence operating system has been developed to synchronize the laser control and data acquisition system with the central control system（CSS）. The system operates commands from the CSS of J-TEXT and generates triggers for the laser and data acquisition system in the proper sequence. It also measures an asynchronous time value that is needed for accurate time stamping. All functions are served by a field-programmable gate array development platform that is suitable for high-speed data and signal processing applications.Several embedded peripherals, including Ethernet and USB 2.0, provide communication with the CSS and the server.展开更多
Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only cha...Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.展开更多
基金supported by the National Magnetic Confinement Fusion Science Program of China under Contract No.2015GB111001by National Natural Science Foundation of China(Grant No.11575067)
文摘A Thomson scattering diagnostic system is under construction at the Joint Texas Experimental Tokamak（J-TEXT）. A 1064 nm Nd：YAG laser with 50 Hz repetition rate is used as the laser source. We have used a software for careful and precise control of the laser through serial communication. A time sequence operating system has been developed to synchronize the laser control and data acquisition system with the central control system（CSS）. The system operates commands from the CSS of J-TEXT and generates triggers for the laser and data acquisition system in the proper sequence. It also measures an asynchronous time value that is needed for accurate time stamping. All functions are served by a field-programmable gate array development platform that is suitable for high-speed data and signal processing applications.Several embedded peripherals, including Ethernet and USB 2.0, provide communication with the CSS and the server.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62063004 and 61762033in part by the Hainan Provincial Natural Science Foundation of China under Grant 2019RC018 and 619QN246by the Postdoctoral Science Foundation under Grant 2020TQ0293.
文摘Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.
