An Iterative Optimization Approach for Fuzzy Predictive Control

International Journal of Control, Automation and Systems - This paper proposes an iterative approach in fuzzy model predictive control. When the prediction model is nonlinear or uncertain,...     

基于信息熵的不完备数据特征选择算法

在分析已有不完备信息熵的基础上,提出一种基于相似关系的不完备信息熵,并证明该信息熵的若干性质.给出一个不完备数据特征选择算法,算法以改进的不完备信息熵作为特征选择准则,直接对不完备数据的特征进行熵值分析,并采用顺序前向浮动选择方法解决特征间的相关性问题.最后在UCI实测数据集上的实验表明,文中算法具有更高的准确率和更快的特征选择速度.     

An Iterative Multiresolution Scheme for SFM with Missing Data

Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix. Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported. Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.

基于直方图和交叉熵的文本图像识别方法

从人的认知特性出发,提出了一种基于交叉熵的文本图像识别方法.首先,把原图像和它的平滑图像组合起来构成广义图像,广义图像的直方图称为广义直方图;其次,利用广义直方图计算广义图像二值化前后的交又熵,得到图像最佳二值化阈值;最后,由最佳二值化阈值下的交叉熵和设定的交叉熵门限进行比较,实现文本图像的识别.实验结果与性能比较表明,本文方法在保证很高识剐率的同时,极大地降低了虚报率.     

基于应用层的实时媒体流多播QoS的自适应方法

该文提出的应用层多播QoS自适应方法建立在由多个单播HPF路径构成的多播树基础上,多播组成员节点在一个HPF会话中是接收者,而可能在其它一些HPF会话中作为转发器。多播树由最大瓶颈带宽生成树算法生成,媒体流的传输协议采用HPF,由最大瓶颈带宽生成树上的分支节点作转发器来实现速率自适应和包丢弃算法,以达到整个多播组的平均带宽利用最大化。     

An Approach for Establishing Enterprise Data Standards

Avoid the common pitfalls experienced by many organizations that have established enterprise data standards by following a straightforward process. Standards should be defined around a simple framework and be borrowed from third parties or vendors wherever possible to save time. It is better to reuse, rather than reinvent or create standards. To be used successfully, standards must be easy to understand, have a proven track record, be easy to use, and be readily available. Standards should be maintained in a central repository such as Lotus Notes or on an intranet Web site so that they are readily accessible to project teams.     

基于数据挖掘技术的智能化入侵检测模型

给出了一个基于数据挖掘技术智能化入侵检测模型,它由若干个对等式执行入侵检测功能的单元IDU（intrusion detection unit)组成,每个IDU参照通用入侵检测轮廓CIDF（common intrusion detection framcwork)构建,该模型采用关联规则,序列规则对数据进行挖掘,用判定树分类技术对获得的模式进行分类,实验表明,该模型具有较好的效益。     

基于数据挖掘技术的智能入侵检测模型

数据挖掘能从大量的、有噪声的、随机的数据中提取隐含在其中有用的信息和知识,而Agent代理技术具有功能的连续性、自主性、适应性,能够连续不断地感知外界及自身状态的变化。针对现有入侵检测系统的不足,对数据挖掘技术和智能检测代理应用于入侵检测系统进行了研究,提出一个基于数据挖掘技术的智能入侵检测系统模型,探讨了模型体系结构及主要功能。实验表明,该模型能生成新规则,找到入侵数据,降低入侵检测系统的误警率。     

面向Deep Web本地化数据集成的数据源两层选择模型

针对基于数据源质量选择方法的数据源在数据爬取时存在代价大、重复率高的问题,提出一种结合两层选择模型的Deep Web数据源选择和集成方法。该方法根据数据源本身质量和数据源的效用构建数据源的两层选择模型。给出基于该模型的递归增量数据源选择和集成策略,采用基于数据源质量的选择器过滤大量低质量Deep Web数据源,仅选择若干个高质量的数据源作为第2层选择器的输入。从候选数据源集合中递归地选择,使集成系统在获得尽可能多的高质量数据的同时,避免出现较高覆盖率的k个数据源,作为集成系统最终需要爬取和集成的数据源。实验结果表明,该方法结合两类选择器的优点,缩减了候选数据源的空间并保证集成数据的质量,同时避免了系统处理大量重复数据,有效降低Deep Web数据爬取与集成的代价。     

一种改进的最小互熵门限法

针对Ｌｉ＆Ｌｅｅ法存在的缺陷,本文提出了一种改进的最小互熵门限法,理论分析和实验结果表明改进算法具有更好的有效性。     

An Iterative Clustering Approach Based on Material Flow Requirements for Cellular Designs

This paper presents an iterative clustering process involving the addition of cells to a proposed cellular design to determine the best assignment of machines to cells. The assignment of machines to each cell is based on material flow clustering techniques with the objective of minimizing inter-cell flows. To determine the performance of the proposed clustering approach, comparisons are made with an Exhaustive Search (ES) approach to show the relative optimality.     

一种改进的子空间选择算法在聚类中的应用

高维空间聚类由于其数据分布稀疏、噪声数据多、"差距趋于零现象"等特征,因而给传统的聚类方法带来很大的阻碍.为了解决这些问题,介绍了一种对于潜在子空间的选择方法.原有方法采用k邻近点的原理,根据不同聚簇分布情况在k邻近点统计应用上的不同特征,制定出衡量子空间优劣的标准,从而获得进一步进行简单聚类的子空间.结合近似邻近点搜索方法对其进行改进,改进后的子空间选择方法能更好地探测到聚簇的存在情况,并有效地拓展了子空间聚类算法的应用范围.实验结合生物统计学上的基因变异数据集进一步证实了方法的有效性和可行性.     

一种实时数据库系统的面向对象模型

提出了一种实时数据库系统的面向对象模型,着重讨论扩展了定时限制的对象模型,关系模型,事务模型。     

一种UDP报文数据的搜索方法

在分布式数据传输中,UDP协议由于实现机制简单、传输效率高,被大量应用系统所采用。为了数据分析和处理的需要,常常要对录取得到的海量UDP报文数据进行搜索,以得到符合要求的报文。针对以上需求,文章在UDP报文数据的搜索中引入倒排索引机制。从存储、生成两个方面对UDP报文倒排索引进行了详细论述,并介绍了利用UDP报文倒排索引的报文搜索方法。     

An Efficient Density-based Approach for Data Mining Tasks

We propose a locally adaptive technique to address the problem of setting the bandwidth parameters for kernel density estimation. Our technique is efficient and can be performed in only two dataset passes. We also show how to apply our technique to efficiently solve range query approximation, classification and clustering problems for very large datasets. We validate the efficiency and accuracy of our technique by presenting experimental results on a variety of both synthetic and real datasets.     

A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering

This paper presents an unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture model that is able to extract independent and non-Gaussian features without loss of accuracy. The proposed model is learned using the Expectation-Maximization algorithm by minimizing the message length of the data set. Experimental results show the merits of the proposed methodology in the categorization of object images.     

Technology Selection: An Educational Approach

Creating and enhancing a software engineering work force requires several different types of continuing education for software professionals, including: task-oriented education, enhancement-oriented education and selection-oriented education. In this paper, we focus on the important, but often neglected, category of selection-oriented education. We begin with a discussion of technology selection, indicating what it involves, how it contributes to improving the state of practice, and why it is key to technology improvement in general. This is followed by a discussion of some criteria for selection-oriented education programs. We then describe the selection-oriented education activities at the Rocky Mountain Institute of Software Engineering and relate some problems encountered in establishing these activities.     

一种改进的中文文本特征选择方法

目前常用向量空间模型VSM表示文档,造成的高维问题制约其实际应用的效果。在研究现有的特征降维技术的基础上,对部分常用的特征提取方法做简要的分析,综合类间集中度、类内分散度、反文档频率,提出一种新的特征选择方法,通过实验验证该特征选择方法的有效性。     

群组决策系统的熵模型

应用拓展熵的概念研究了群组决策支持水平,个体决策可靠性理论及其应用问题,得到“水平”和“可靠性”测度的一系列公式和算法,从而开辟了群组和个体决策支持系统优化的新途径。