一种基于横切特征分析的软件体系结构自动重构方法

软件体系结构中的横切关注点增加了软件体系结构的复杂性,从而加剧了体系结构演化与维护的困难.这种设计问题可以通过体系结构层面的重构来进行改善.在已有的横切特征分析方法基础上,提出了一种面向横切特征分析的体系结构自动重构方法.该方法首先基于特征与构件之间的追踪关系分析横切特征,然后将与横切特征有直接追踪关系的构件从初始体系结构中提取出来,实现方面构件,完成体系结构重构.在面向方面体系结构描述语言AO-ADL基础上开发了相应的体系结构重构工具,并针对一个业务系统进行了体系结构重构实验.实验结果表明,该方法能有效地实现体系结构横切特征的自动化重构.     

基于Trustie资源库的导航软件构件管理

结合导航软件生产现状与制约因素,提出对导航软件进行构件化改造,形成10大类120个共享构件。同时,利用Trustie已有成果,首先对Trustie构件资源库进行适应性改造,使之能适应导航软件生产;然后对导航软件构件在进入Trustie构件资源库之前,进行可信分级评估;最后利用该构件资源库,实现对导航软件构件的登记、查询、统计等管理,建立起导航构件资源库。该资源库的建立,极大地提高导航软件构件的可复用率,方便对构件的分类与管理,完成各类构件在导航软件协同生产环境中共享,为实现导航软件的大规模生产与敏捷制造奠定了基础。     

CAE在同步辐射装置挡光元件设计中的应用

针对第三代同步辐射装置挡光元件承受极高热负载、设计难度大的问题,综述CAE在其设计中的应用情况:借助有限元分析可以获得挡光元件关键部件吸收体的最高温度和最大应力这两个最主要设计参数;通过计算流体力学(Computational Fluid Dynamics,CFD)软件模拟可以获得吸收体中冷却管道的对流换热与流动阻力特性参数;采用热弹塑性有限元分析可以获得用于低周疲劳寿命预估的吸收体热应力应变迟滞循环.下一步工作将围绕当今研究的核心问题——基于低周疲劳的设计准则展开,包括同步辐射高热负载作用下低周疲劳裂纹的起裂寿命和扩展寿命预测,以及吸收体倾角、表面光滑度、冷却管道排布等对寿命的影响.这些研究均需要充分利用CAE的强大分析功能.     

集装箱码头三维仿真组件平台

为直观、形象地研究集装箱码头生产优化问题,采用三维仿真的方法实现集装箱码头物流信息与作业过程的可视化．基于不同业务流程的仿真要求,合理地划分静态布局、装卸设备、装卸对象、码头操作、运动学解算、数据访问、通信接口和图形渲染等组件的粒度;建立层次结构形式的组件平台,体现客观对象的逻辑关系;遵循标准COM＋规范设计岸桥、场桥和集卡等集装箱码头主要三维仿真组件．以上海振华重工（集团）股份有限公司自动化集装箱码头为例验证该组件平台的可操作性,结果表明该方法对集装箱码头装卸工艺的改进和管理水平的提升具有实际指导意义．     

基于图像分块的二维投影特征提取能力研究

人脸识别研究的主要目的是提高识别率,关键技术在于提取有效的人脸特征。提出了分块多投影和分块双向多投影二维特征提取方法。分块多投影特征提取方法,针对现有分块单投影特征提取方法中每一子图均采用相同投影矩阵,而对人脸局部信息不加以区别,利用二维主成分分析方法,构造了分块多投影矩阵,使不同的子图投影到不同的子空间,与传统二维主成分方法和分块单投影方法相比,有效地利用人脸局部信息,降低了特征维数,提高了识别率,在ORL人脸库上实验表明了其有效性。     

基于控件编程的上位机矿井操车监控系统

本文从矿井操车系统着手,针对矿井操车监控特点设计系统控件,在这些控件的基础上完成上位机软件监控,软件能实时监控系统运行状况,可远程监控,故障智能检测,维护量较小,有一定的推广价值。     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

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.     

Mixtures of robust probabilistic principal component analyzers

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to extract the local principal orientations in the data. An important issue with this generative model is its sensitivity to data lying off the low-dimensional manifold. In order to address this problem, the mixtures of robust probabilistic principal component analyzers are introduced. They take care of atypical points by means of a long tail distribution, the Student-t. It is shown that the resulting mixture model is an extension of the mixture of Gaussians, suitable for both robust clustering and dimensionality reduction. Finally, we briefly discuss how to construct a robust version of the closely related mixture of factor analyzers.     

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.