开关设备电弧故障检测及定位装置

论述了开关设备电弧故障检测及定位装置,其中包括几个功能模块下各单元的设计,结合故障电流检测法、弧光探测法和电弧光成像原理及虚拟仪器平台技术,对开关设备内电弧故障进行检测和定位,使之能很好地反映开关设备内部电弧故障发生和发展过程,为后续的电弧故障分析提供依据。     

异平台雷达和ESM的高精度协同估计方法

随着网络的应用,不同平台的协同作战成为一种趋势,该趋势促成了异类平台间的数据融合需求.作为典型的协同模式,异平台的雷达和ESM观测的坐标系不同,无法直接融合,且二维ESM数据和三维雷达数据直接融合的精度较低.论文提出将雷达距离及方差转换到ESM坐标系下,转换得到虚假的距离量测结合ESM的角度量测,在三维坐标下进行Kalman滤波,获得ESM的估计及协方差矩阵,再进行协方差交集估计处理,可以较大的提高估计精度.     

基于ArcGlobe的三维数字城市建模研究与实现

论文从实际的数字城市项目出发,采用ArcGlobe10和3DS Max相结合的模式,依照“依图而建”的思想,建立城市三维模型.重点对三维模型构建中数据处理,数据导人,数据转换集成,系统优化等所遇到的技术问题进行阐述与解决.论文提供了一种搭建大三维场景的方法.     

多速率数字语音保密通信系统硬件设计

论文针对复杂的无线信道环境,设计实现了高质量、低误码率的语音保密通信系统.该系统完成一个发送通路和一个接收通路,可以完成一个完整的自环语音通信.该系统中,可供选择的数据编码速率为2000、3600、4000、4800、6400、7200、8000和9600bps.当外部干扰比较小时,可以选用较高的数据速率;当外部干扰较大时,可以选用较低的数据速率,以牺牲数据速率来提高语音通信的质量.     

分布式仿真系统的数据监控软件的实现

文中以高速列车数字化仿真平台为研究背景,设计并实现了基于Oracle数据库的分布式仿真系统数据监控软件。该系统采用VisualC++和LabWindows/CVI混合编程的方式以及多线程技术,实现了对分布式仿真系统中仿真工况的状态查询及监控,仿真工况模型的下载及图形化显示,待监测工况参数的灵活配置及多通道数据曲线动态显示,报警提示等功能。能及时、直观、有效地展示出仿真系统中各仿真工况的仿真过程及结果,使系统操作人员能及时了解分布式仿真系统中各工况的工作状态。     

A general method of spatio-temporal clustering analysis

Spatio-temporal clustering has been a hot topic in the feld of spatio-temporal data mining and knowledge discovery.It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world.However,existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities,and the coupling in space and time has not been well highlighted.In this paper,a unifed framework for the clustering analysis of spatio-temporal data is proposed,and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology.Our method is applied successfully to fnding spatio-temporal cluster in China’s annual temperature database for the period 1951 1992.     

Data Security Model for Cloud Computing

From the perspective of data security, which has always been an important aspect of quality of service, cloud computing focuses a new challenging security threats. Therefore, a data security model must solve the most challenges of cloud computing security. The proposed data security model provides a single default gateway as a platform. It used to secure sensitive user data across multiple public and private cloud applications, including Salesforce, Chatter, Gmail, and Amazon Web Services, without influencing functionality or performance. Default gateway platform encrypts sensitive data automatically in a real time before sending to the cloud storage without breaking cloud application. It did not effect on user functionality and visibility. If an unauthorized person gets data from cloud storage, he only sees encrypted data. If authorized person accesses successfully in his cloud, the data is decrypted in real time for your use. The default gateway platform must contain strong and fast encryption algorithm, file integrity, malware detection, firewall, tokenization and more. This paper interested about authentication, stronger and faster encryption algorithm, and file integrity.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

基于流行病理论的无线认知传感网抗攻击模型

大量无线通信设备在ISM频段工作致使免许可频段频谱资源稀缺,促使认知无线电和无线传感器网络（WSN）融合,形成无线认知传感器网络（WCSN）。文章研究了无线认知传感器网络中的数据伪造攻击,即恶意次用户通过向其他次用户发送伪造的频谱感知数据,导致控制中心做出错误的频谱分配决策。基于流行病理论,文章对数据伪造攻击中的信息传播过程进行了建模和分析,找出了决定无线认知传感器网络中潜在流行病爆发的关键因素。最后,通过仿真实验,对模型进行了验证,并对系统动态特性进行了研究。