基于加速度计的路形计算方法研究

传统的路形测量方法测量过程复杂且测量精度低,为了降低路形测量的难度以及提高路形计算的准确性,提出了一种基于加速度计的路形计算方法,主要由2个加速度计、一个激光位移传感器组成的测量系统为硬件条件,以加速度计所检测到的检测梁纵向加速度信号作为计算机主要输入信号,经过MATLAB软件滤波处理并对其进行二次积分所得到的检测梁纵向位移作为主要计算参数而求得道路的纵断面形状.在真实的道路上进行了验证,结果表明,所求得的路形与实际的路形接近,吻合度较好.计算方法具有结构简单,精度较高等优点,可满足对大多数路面形状的测量需求.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

基于概念的形状分类与识别

基于内容的图像检索已经成为图像检索的一个重要研究方向。其中利用形状特征可以实现形状较为明显的各类图像库的检索。该文对图像形状特征的提取和描述方法进行了较深入地研究,选取其中一部分特征值,分别利用BP神经网络和支持向量机进行形状识别,取得令人满意的实验效果。     

一种分布式拒绝服务攻击的检测方法

该文提出和实现了一种基于网络异常流量特征的检测模型。通过将多条链路或多个流的流量信号作为一个整体进行研究,构建了网络异常流量监控的系统模型。该模型包括了数据采集、分析、异常判断和警告等功能,并综合了异常信息融合及警告信息关联性分析技术。通过实验证明,该套系统模型对短时间段内的突发流量能提供有效的检测和报警服务。     

用Packet Tracer模拟软件提高《网络互联技术》实验教学效果

实验教学是高职计算机网络课程的重要组成部分,目前高职计算机网络实验课程开展困难,效果不明显。该文结合当前高职计算机网络课程实验教学的现状,谈一谈如何利用Packet Tracer模拟软件提高《网络互联技术》课程的实验教学效果。     

基于混合式结构的无线Mesh网的改进AODV路由协议

对AODV协议做了一些改进,通过优先使用Mesh路由器来传送报文．以充分利用混合结构的异构性。为了实现多射频Mesh网络的通道多样性和减少干扰．设计了一个简单的通道选择机制。     

基于C8051F121在智能车辆检测器中的设计与应用

随着我国城市道路交通的发展,汽车数量不断上升,使得城市交通拥挤和交通事故日显突出,为了能够改善混乱的交通状况,缓解道路拥堵、改善出行条件、提高运输效率、提高道路安全性,为此,人们提出了智能车辆检测器,以此来解决日益突出的交通问题.本文基于美国Silicon Lab公司的CIP-51TM微控制器核的片上系统(SOC)C8051F121芯片为核心的硬件平台,文中对设备的硬件电路设计,接口电路设计和软件设计进行全方面的论述和研究.实验结果证明该设备具有低功耗、低成本、功能强大的特点,充分利用了该芯片提供的资源,并且满足了智能车辆检测器的需求.     

室内有害气体远程报警系统设计

随着人们生活水平的日益提高,人们对家居环境的要求也在不断的提升中,本文针对室内危害气体泄露危机人身安全的问题,设计了一套专门用来监测居室内有危害气体发生泄露时的远程报警系统.本系统主要应用了传感技术、GSM和SMS技术,实时监测CO气体是否发生泄露,当出现泄漏时,实时进行远程报警.对系统进行测试结果表明,设计实现了预定目标.     

基于经济的网格资源调度算法研究

网格使构建虚拟组织,在全世界范围内共享资源成为现实.但是,在网格环境下的资源管理和调度是一项相当复杂的工作,这主要是由于资源分布在世界各地,隶属于不同的组织,各个资源拥有者都有不同的使用和访问策略、开销模型、不断变化的负载和可用性.为了满足网格计算环境中资源调度的复杂需要,可以将现实世界中的经济原理和模型应用到网格环境下的任务调度中,并据此提出了几种经济资源调度算法.     

基于VB开发试题库系统

系统以英语教育为背景进行设计,旨在提供一个适合形成性测试和阶段性测试的自动组卷系统,能快速方便地提供各种要求的试卷,帮助教师把握教学的进度,及时地反馈教学中的问题,以使改进教学方法和调整教学重点。