Hefei Light Source(HLS)is a synchrotron radiation light source that primarily produces vacuum ultraviolet and soft X-rays.It currently consists of ten experimental stations,including a soft X-ray microscopy station.As...Hefei Light Source(HLS)is a synchrotron radiation light source that primarily produces vacuum ultraviolet and soft X-rays.It currently consists of ten experimental stations,including a soft X-ray microscopy station.As part of its on-going efforts to establish a centralized scientific data management platform,HLS is in the process of developing a test sys-tem that covers the entire lifecycle of scientific data,including data generation,acquisition,processing,analysis,and de-struction.However,the instruments used in the soft X-ray microscopy experimental station rely on commercial propriet-ary software for data acquisition and processing.We developed a semi-automatic data acquisition program to facilitate the integration of soft X-ray microscopy stations into a centralized scientific data management platform.Additionally,we cre-ated an online data processing platform to assist users in analyzing their scientific data.The system we developed and de-ployed meets the design requirements,successfully integrating the soft X-ray microscopy station into the full lifecycle management of scientific data.展开更多
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
印度近十年工业化进程发展迅速,已经完全进入了工业化建设阶段,因此及时掌握印度地区的工业化发展进程对于印度的工业化发展具有十分重要的意义。本研究基于375 m高时空分辨率的NPP-VIIRS主动式火点数据(NPP-VIIRS active fire/hotpot d...印度近十年工业化进程发展迅速,已经完全进入了工业化建设阶段,因此及时掌握印度地区的工业化发展进程对于印度的工业化发展具有十分重要的意义。本研究基于375 m高时空分辨率的NPP-VIIRS主动式火点数据(NPP-VIIRS active fire/hotpot data),利用改进的K-means时空密度分割算法,结合夜间灯光数据过滤,并叠加Google Earth高分辨率影像数据验证,形成了印度地区2012–2021年间的工业热源对象数据集。经人工验证分析,本数据集识别准确率为93.32%,与同期数据相比识别准确率、个数、颗粒度和空间重叠度有显著提升。本数据集提供了工业热源的四角经纬度、区域内火点数量、所属行政区、起止运行年份等信息,对分析印度地区的工业化进程提供了科学可靠的数据支撑,同时也为印度地区未来发展和促进中印合作提供了一定的决策支持。展开更多
基金supported by the Fundamental Research Funds for the Central Universities(WK2310000102)。
文摘Hefei Light Source(HLS)is a synchrotron radiation light source that primarily produces vacuum ultraviolet and soft X-rays.It currently consists of ten experimental stations,including a soft X-ray microscopy station.As part of its on-going efforts to establish a centralized scientific data management platform,HLS is in the process of developing a test sys-tem that covers the entire lifecycle of scientific data,including data generation,acquisition,processing,analysis,and de-struction.However,the instruments used in the soft X-ray microscopy experimental station rely on commercial propriet-ary software for data acquisition and processing.We developed a semi-automatic data acquisition program to facilitate the integration of soft X-ray microscopy stations into a centralized scientific data management platform.Additionally,we cre-ated an online data processing platform to assist users in analyzing their scientific data.The system we developed and de-ployed meets the design requirements,successfully integrating the soft X-ray microscopy station into the full lifecycle management of scientific data.
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
