为增强电子档案资料管理水平,设计一种基于单标签射频识别(radio frequency identification,RFID)的电子档案分类管理系统。使用RFID搭建系统框架,通过中心数据库、终端管理器模块实现RFID数据存储与传输,运用文件管理、档案管理、开发...为增强电子档案资料管理水平,设计一种基于单标签射频识别(radio frequency identification,RFID)的电子档案分类管理系统。使用RFID搭建系统框架,通过中心数据库、终端管理器模块实现RFID数据存储与传输,运用文件管理、档案管理、开发利用及系统维护4个模块完成电子档案分类管理日常运维;引入模糊聚类算法提取电子档案数据信息熵,使用关联规则实现数据融合与自主调度,特征分解数据运行状态信息,并通过神经网络组建分类器对电子档案分类。实验结果证明:该系统运行时能实现高负载均衡,且CPU利用率低,在分类管理方面拥有准确率高、响应速率快等优势。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
为了更好地实践云计算提供廉价按需服务的宗旨,提出了一种在模糊聚类基础上,基于两级调度模式的任务调度(FCTLBS,fuzzy clustering and two level based task scheduling)算法,新算法设置用户调度和任务调度2个等级。对资源进行性能模...为了更好地实践云计算提供廉价按需服务的宗旨,提出了一种在模糊聚类基础上,基于两级调度模式的任务调度(FCTLBS,fuzzy clustering and two level based task scheduling)算法,新算法设置用户调度和任务调度2个等级。对资源进行性能模糊聚类;根据任务参数计算资源偏好,使不同偏好任务在不同聚类中选择,缩小了选择范围,更好地反映了任务需求。仿真实验表明,本算法较之同类算法具备一定的优越性。展开更多
文摘为增强电子档案资料管理水平,设计一种基于单标签射频识别(radio frequency identification,RFID)的电子档案分类管理系统。使用RFID搭建系统框架,通过中心数据库、终端管理器模块实现RFID数据存储与传输,运用文件管理、档案管理、开发利用及系统维护4个模块完成电子档案分类管理日常运维;引入模糊聚类算法提取电子档案数据信息熵,使用关联规则实现数据融合与自主调度,特征分解数据运行状态信息,并通过神经网络组建分类器对电子档案分类。实验结果证明:该系统运行时能实现高负载均衡,且CPU利用率低,在分类管理方面拥有准确率高、响应速率快等优势。
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.
文摘为了更好地实践云计算提供廉价按需服务的宗旨,提出了一种在模糊聚类基础上,基于两级调度模式的任务调度(FCTLBS,fuzzy clustering and two level based task scheduling)算法,新算法设置用户调度和任务调度2个等级。对资源进行性能模糊聚类;根据任务参数计算资源偏好,使不同偏好任务在不同聚类中选择,缩小了选择范围,更好地反映了任务需求。仿真实验表明,本算法较之同类算法具备一定的优越性。
