An online plasma shape reconstruction, based on the offiine version of the EFIT code and MPI library, can be carried out between two adjacent shots in EAST. It combines online data acquisition, parallel calculation, a...An online plasma shape reconstruction, based on the offiine version of the EFIT code and MPI library, can be carried out between two adjacent shots in EAST. It combines online data acquisition, parallel calculation, and data storage together. The program on the master node of the cluster detects the termination of the discharge promptly, reads diagnostic data from the EAST mdsplus server on the completion of data storing, and writes the results onto the EFIT mdsplus server after the calculation is finished. These processes run automatically on a nine-nodes IBM blade center. The total time elapsed is about 1 second to several minutes, depending on the duration of the shot. With the results stored in the mdsplus server, it is convenient for operators and physicists to analyze the behavior of plasma using visualization tools.展开更多
This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real...This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.展开更多
基金supported by National Natural Science Foundation of China (No.10835009)the Knowledge Innovation Program of The Chinese Academy of Sciences (No.KJCX3.SYW.N4)
文摘An online plasma shape reconstruction, based on the offiine version of the EFIT code and MPI library, can be carried out between two adjacent shots in EAST. It combines online data acquisition, parallel calculation, and data storage together. The program on the master node of the cluster detects the termination of the discharge promptly, reads diagnostic data from the EAST mdsplus server on the completion of data storing, and writes the results onto the EFIT mdsplus server after the calculation is finished. These processes run automatically on a nine-nodes IBM blade center. The total time elapsed is about 1 second to several minutes, depending on the duration of the shot. With the results stored in the mdsplus server, it is convenient for operators and physicists to analyze the behavior of plasma using visualization tools.
基金This work was supported in part by the National Natural Science Foundation of China under Grant U20A20225,61833013in part by Shaanxi Provincial Key Research and Development Program under Grant 2022-GY111.
文摘This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.
