基于Moodle的网络教学模式构建与使用

本文介绍一种功能强大、开源免费的学习管理系统(LMS)- -Moodle,阐述了利用该系统的功能模块进行课程设计的方法和构建网络教学模式的流程,并提出了这种教学模式在实际使用过程中的建议.     

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.     

改进自组织映射神经网络在指纹识别中的应用

改进自组织映射神经网络方法是将常规自组织映射神经网络方法结合确定性水平,对网络的输入矢量进行预处理。通过实验比较了这种改进的自组织映射神经网络识别方法与常规的自组织映射神经网络识别方法的识别效果,在识别性能上有了很大的提高。     

一种改善无线TCP性能的链路层重传方案

提出一种提高无线环境中TCP性能的链路层选择性重传的改进方案mARQ.根据每个数据包在链路层被拆分成多个无线帧这一特点,对包中所有帧的重传次数之和进行限定,使其可以根据各帧的需求进行自适应分配.系统仿真表明:与传统方案相比,新设计方案mARQ在高误帧率的环境下以较低的误包率获得了较高的TCP吞吐量,提高了无线环境下TCP的性能.     

基于阈值分割的运矿车辆车牌精确定位

由于运矿车辆车牌图像质量低、车牌污染严重、环境复杂、识别率低等特殊情况,设计了一种基于灰度阈值分割的运矿车辆车牌的精确定位算法.它通过对粗定位车牌图像进行灰度阈值分割,得到估计车牌区域并进行区域标记,根据车牌所在连通区域面积最大这一特征,得到完整有效的车牌区域,结合车牌先验知识去掉其边框,从而得到车牌的精确定位结果.实验结果表明,该算法定位准确率高,耗时少,分割效果好,具有较强的实用价值,是对现有车牌识别技术有意义的扩展和补充.     

潜在属性空间树分类器

提出一种潜在属性空间树分类器(latent attribute space tree classifier,简称LAST)框架,通过将原属性空间变换到更容易分离数据或更符合决策树分类特点的潜在属性空间,突破传统决策树算法的决策面局限,改善树分类器的泛化性能.在LAST 框架下,提出了两种奇异值分解斜决策树(SVD (singular value decomposition) oblique decision tree,简称SODT)算法,通过对全局或局部数据进行奇异值分解,构建正交的潜在属性空间,然后在潜在属性空间内构建传统的单变量决策树或树节点,从而间接获得原空间内近似最优的斜决策树.SODT 算法既能够处理整体数据与局部数据分布相同或不同的数据集,又可以充分利用有标签和无标签数据的结构信息,分类结果不受样本随机重排的影响,而且时间复杂度还与单变量决策树算法相同.在复杂数据集上的实验结果表明,与传统的单变量决策树算法和其他斜决策树算法相比,SODT 算法的分类准确率更高,构建的决策树大小更稳定,整体分类性能更鲁棒,决策树构建时间与C4.5 算法相近,而远小于其他斜决策树算法.     

基于近似高斯金字塔的视觉注意模型快速算法

利用输入图像的近似高斯金字塔,将经典的基于显著性的视觉注意模型改造为时空开销更小的版本,从而使其更加适合在嵌入式实时系统中实现.首先采用矩形窗口近似圆形窗口,矩形平均算子近似高斯卷积核;然后采用“先做行累加,再做列累加”的方法来实现矩形平均算子,并直接采样计算出各个特征通道的显著性分布图,该算法关于输入图像像素点个数具有线性时间复杂度;最后,还给出了在显著性分布图中抑制已提取区域显著性的快速算法.在Berkeley分割图像库上的实验结果表明,该方法极大地减小了系统实现的时空开销,且输出结果的误差在可接受范围内.提出的用矩形窗口近似圆形窗口,用矩形平均算子近似高斯卷积核的方法,还适用于其他需要在嵌入式实时系统中实现的图像处理问题.     

改进信息化武器系统性能的仿真博弈混合方法

用定量分析的方法改进性能是信息化武器系统开发和运用中必须解决的重要问题,借助仿真和博弈分析的混合方法,在对传统混合策略纳什均衡原理应用的基础上,构建基于二人非零和(TPNZS)非合作博弈模型来改进信息化武器系统的性能,对典型信息化武器系统-舰载导弹防空系统的初步研究表明:用识别正确、错误目标能力以及导弹命中目标能力描述的舰载导弹防空系统的最优改进性能策略为△*=(0,△2*,1-△2*)=(0.0000,0.7118,0.2882).     

基于LabVIEW的催化剂程序升温表征系统应用软件开发

开发基于LabVIEW的程序升温分析应用软件,主要用于催化剂程序升温表征实验.用AI自控仪表监控气路和电路的主要参数.基于LabVIEW的软件控制和采集主要参数.软件数据分析部分可分析采集程序升温图形的峰信息.测试了工业合成气转化用催化剂,表明本系统工作可靠,设计符合要求.     

基于Logistic混沌序列和位交换的图像置乱算法

在分析传统迭代型图像置乱方法不足的基础上,提出一种新的基于混沌序列和位交换的图像置乱算法.算法根据各像素点的位置,采用不同的Logistic混沌序列和像素值的二进制序列进行异或操作改变图像像素值,并利用图像本身的自相关性进行加密,不需迭代,经过一次运算即可得到加密图像.仿真实验结果表明,该算法可有效地实现灰度和彩色图像置乱,并能较好地抵抗椒盐和裁剪攻击,在效率上也优于迭代型置乱方法.