考虑桩顶水平反力的水平受荷单桩弹塑性解析解

基于线性地基反力法和土体的弹塑性本构关系,对桩顶水平反力系数不为零时的水平受荷桩进行了研究,给出了桩顶土屈服临界荷载（水平力、弯矩）的耦合关系式。研究结果表明,当荷载大于临界荷载时,桩顶土发生塑性屈服,其塑性屈服深度随荷载的增加而增加,但其塑性区扩展速率随桩顶荷载的增加而逐渐减小。给出了弹性区与塑性区桩内力耦合的矩阵方程及其迭代求解格式;利用曲面拟合软件给出了塑性区换算深的计算公式。通过工程算例验证,证明该方法可靠快捷,便于手算,值得推广。      

电动振动台试验能力的确认方法研究

研究电动振动台的特性,根据产品振动试验条件提出电动振动台能否满足试验条件的要求,找到电动振动台试验能力范围的确认方法。该文以DC-8000-80电动振动台为实例描述电动振动台能力范围的确认方法研究。     

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.     

一种基于ICA的脊波域数字水印算法

鲁棒性是数字水印的基本要求之一,水印嵌入的位置和强度直接影响到水印的鲁棒性。脊波变换是新型的多尺度分析方法。本文提出的脊波变换是图像在有限Radon变换(FRAT)的基础上进行二维小波变换,将Arnold置乱后的水印嵌入到图像的脊波系数中,利用独立成分分析(ICA)进行水印提取。实验结果表明,在强烈攻击的情况下,此方法具有良好的水印不可见性,对Gaussian、JPEG、Speckle攻击具有良好的鲁棒性。     

构件测试元数据及其提取与展现技术

构件具有的封装性给构件测试带来了困难,而目前构件包含的元数据尚不完整。为了充分利用构件元数据进行构件自动化测试,从构件使用者和测试者的角度设计了内涵丰富的构件元数据,并且针对COM构件,通过访问类型库来自动获取构件结构信息元数据,按层次提取并用XML形式化描述类型信息,得到了COM构件辅助测试规格说明。实例表明,该方法能直接操纵COM构件,自动获取元数据,便于测试脚本自动生成。     

构件组装技术在虚拟实验室中的应用研究

基于构件的软件复用技术是实现软件工程化开发和工业化生产的重要途径,其中构件的集成组装是关键。目前虽然提出了许多构件组装理论,但如何在特定领域根据实际要求实施基于构件组装的软件开发过程仍是值得进一步研究的课题。本文针对计算机硬件类虚拟实验室芯片设备复杂多样、难于开发的问题,在深入分析芯片工作原理与结构特征的基础上,提出一种基于数据驱动的构件组装方法。该方法通过充分重用已有芯片构件以可视化组装的方式开发新芯片构件及建模实验,从而有效提高了开发效率同时软件具有很好的易维护性和可扩展性。     

数据融合技术在废弃弹药分类中的应用

为了对废弃弹药进行分类并安全处理,提出了基于数据融合技术的超声波检测弹体内部成份方法.应用超声检测仪采集大量弹体内部成份样本数据,运用数据融合技术建立神经网络融合模型,对超声波的首波、幅值、频率、增益等特征进行融和处理,从而判断弹药的种类.实验结果表明,该方法准确可靠,具有较高的推广应用价值.     

基于二维图像矩阵的ICA人脸识别

为了解决传统独立分量分析(ICA)在人脸识别过程中存在的高维小样本问题,同时为了提高识别效率,提出了一种基于二维图像矩阵的独立分量分析(ICA)特征提取方法.该方法将人脸图像矩阵作为训练样本,首先利用主分量分析(PCA)对训练样本进行去二阶相关和降维处理,然后对处理后的样本进行ICA特征提取,由于训练样本维数很小,因此它降低了传统ICA方法中高维小样本问题产生的识别错误率,同时减少了识别时间.在Yale人脸库和ORL人脸库上验证了该算法的有效性.     

基于Matlab的自行火炮液压管件应力分析

液压管件作为某型自行火炮液压系统的一类重要元件,其机械性能在很大程度上影响整个液压系统的工作寿命.采用建模和仿真技术研究其应力分布,提出依据广义胡克定律建立液压管件在高强度载荷作用下的力学模型的方法,利用Matlab进行仿真分析,结合仿真结果对液压管件径向及切向应力的分布规律进行了探讨,对于液压管件的疲劳分析以及力学性能研究具有一定的参考价值.     

基于DCT融合2DPCA与DLDA的人脸识别

传统的基于主成分分析的人脸识别需要将图像矩阵转化为向量,特征提取需要花费大最时间.二维主成分分析直接利用图像矩阵,特征提取速度快,但特征数量大,影响分类速度.因此,提出了一种基于离散余弦变换(DCT)的二维主成分分析(2DPCA)和直接线性判决分析(DLDA)结合的人脸识别方法.算法首先用DCT对人脸图像进行压缩并重建,然后利用2DPCA和DLDA对人脸图像进行特征提取.最后选用最近邻分类器进行分类.在ORL人脸库上的测试结果表明,与DLDA或2DPCA算法相比,算法具有更高的识别率.