SQL extension for interval data

IXSQL, an extension to SQL, is proposed for the management of interval data. IXSQL is syntactically and semantically upwards consistent with SQL2. Its specification has been based both on theoretical results and actual user requirements for the management of temporal data, a special case of interval data. Design decisions and implementation issues are also discussed     

An interval arithmetic based fuzzy TOPSIS model

This work suggests a fuzzy TOPSIS model, where ratings of alternatives under criteria and importance weights of criteria are assessed in linguistic values represented by fuzzy numbers. Criteria can be categorized into benefit and cost. Ratings of alternatives versus criteria and the importance weights of criteria are normalized before multiplication. The membership function of each fuzzy weighted rating can be developed by interval arithmetic of fuzzy numbers. A ranking method can then be applied easily to develop positive and negative idea solutions in order to complete the fuzzy TOPSIS model. Finally, a numerical example demonstrates the feasibility of the proposed method.     

基于优先级分组的防碰撞算法

针对在标签数量较多、运动较快的场合,常存在识别效率低且标签漏读率高的问题,提出一种先分组再处理的防碰撞算法——PAJS。该算法按照到达顺序对标签进行分组,以减小漏读率;根据标签识别过程中时隙状况自适应调整帧长度,提高算法的搜索效率;采用跳跃式动态搜索算法处理冲突时隙,从而减少搜索次数和系统传输量。Matlab仿真结果表明,该算法通信复杂度明显小于其他常用算法,吞吐率可达0.59~0.6。在待识别标签较多的场合,该算法优越性更加明显。     

A direct interval extension of TOPSIS method

The TOPSIS method is a technique for order preference by similarity to ideal solution. This technique currently is one of the most popular methods for Multiple Criteria Decision Making (MCDM). The TOPSIS method was primary developed for dealing with only real-valued data. In many cases, it is hard to present precisely the exact ratings of alternatives with respect to local criteria and as a result these ratings are considered as intervals. There are some papers devoted to the interval extensions of TOPSIS method, but these extensions are based on different heuristic approaches to definition of positive and negative ideal solutions. These ideal solutions are presented by real values or intervals, which are not attainable in a decision matrix. Since this is in contradiction with basics of classical TOPSIS method, in this paper we propose a new direct approach to interval extension of TOPSIS method which is free of heuristic assumptions and limitations of known methods. Using numerical examples we show that “direct interval extension of TOPSIS method” may provide the final ranking of alternatives which is substantially different from the results obtained using known methods.     

Maximum diversity/similarity models with extension to part grouping

Maximum diversity/similarity models involve selecting a subset of desired size from a given pool of objects such that some measure of diversity or similarity among the selected objects is maximized. Such models have found application, among other places, in the formation of various groups, the location of obnoxious facilities and the identification of dense subgraphs. We present a mild generalization, which yields a stronger and more practical formulation and is seen to be solved effectively. As an extension, we consider the part‐grouping problem as encountered in manufacturing cell formation. We show how the enhanced diversity/similarity model can be used to form part families in a way that is both flexible and competitive with existing approaches. We provide an illustrative example and limited computational results.     

基于分组的启发式SAT新算法——DC&DS算法

目前提高求解SAT问题完全算法的计算效率问题已成为挑战性研究问题。提出了一种基于启发式分组的SAT完备算法。启发式分组策略将一个全局搜索问题,转为局部搜索问题。并将该策略引入到结合BDD与SAT算法的形式验证中,与一般的启发式SAT算法相比,该算法在求解速度和求解问题的规模等方面都明显地改进了,实验结果表明了该算法的可行性和有效性。     

基于标签分组的RFID防碰撞算法

针对无线射频识别技术中的读写碰撞问题提出了一种新的标签防碰撞算法。该算法利用陪集分解理论将电子标签分组,限定每组标签在规定的时隙响应。根据查询码和碰撞特征位,阅读器可在一个时隙内识别出一组标签。Matlab仿真结果表明,在多标签时该算法的时隙利用率和吞吐率优于二进制搜索算法和动态帧时隙算法。     

基于散列的频繁项集分组算法

Apriori算法是频繁项集挖掘的经典算法。针对Apriori算法的剪枝操作和多次扫描数据集的缺点,提出了基于散列的频繁项集分组(HFG)算法。证明了2-项集剪枝性质,采用散列技术存储频繁2-项集,将Apriori算法剪枝操作的时间复杂度从O(k×|Lk|)降低到O(1);定义了首项的子项集概念,将数据集划分为以Ii为首项的数据子集并采用分组索引表存储,在求以Ii为首项的频繁项集时,只扫描以Ii为首项的数据子集,减少了对数据集扫描的时间代价。实验结果表明,由于HFG算法的剪枝操作产生了累积效益,以及分组扫描排除了无效的项集和元组,使得HFG算法在时间性能方面与Apriori算法相比有较大提高。     

基于Chwa&Hakimi故障模型的集团诊断

提出了基于Chwa＆Hakimi故障模型的集团诊断,定义了绝对故障集和最终诊断图,由此能找到所有的基于Chwa＆Hakimi故障模型的相容故障模式。从仿真结果看大大减少了系统级故障诊断的复杂度,特别是对强t-可诊断系统,具有较大的实际意义。     

基于结果类型分组的XML关键字查询*

提出了基于结果类型分组的XML(extensible markup language,可扩展标志语言)关键字查询算法。采用熵值赋权法确定结果类型,继而对XML文档节点虚拟分组,并在虚拟组的基础上给出了相应的查询算法,不仅确保了结果信息的完整,避免了丢失某些有意义结果和返回无意义结果的现象。实验结果表明,所提出算法与SLCA、MLCEA相比,在查询质量、效率及稳定性上有一定提高。     

Intrusion detection using reduced-size RNN based on feature grouping

Intrusion detection is well-known as an essential component to secure the systems in Information and Communication Technology (ICT). Based on the type of analyzing events, two kinds of Intrusion Detection Systems (IDS) have been proposed: anomaly-based and misuse-based. In this paper, three-layer Recurrent Neural Network (RNN) architecture with categorized features as inputs and attack types as outputs of RNN is proposed as misuse-based IDS. The input features are categorized to basic features, content features, time-based traffic features, and host-based traffic features. The attack types are classified to Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). For this purpose, in this study, we use the 41 features per connection defined by International Knowledge Discovery and Data mining group (KDD). The RNN has an extra output which corresponds to normal class (no attack). The connections between the nodes of two hidden layers of RNN are considered partial. Experimental results show that the proposed model is able to improve classification rate, particularly in R2L attacks. This method also offers better Detection Rate (DR) and Cost Per Example (CPE) when compared to similar related works and also the simulated Multi-Layer Perceptron (MLP) and Elman-based intrusion detectors. On the other hand, False Alarm Rate (FAR) of the proposed model is not degraded significantly when compared to some recent machine learning methods.     

基于Nystrom方法的图像谱分割算法的聚类改进

基于图论的图像谱分割是近年来国际图像分割领域的一个热点,但必须构造亲和力矩阵,而构造亲和力矩阵要耗费大量的计算时间,于是有研究者提出可以用Nystrom采样对亲和力矩阵及其主特征向量估计实现最终的图像分割.对Nystrom方法用于对亲和力矩阵及其主特征向量的估计进行介绍,并对此进行了具体的实现及分析.针对用传统的k-means方法实现对最后的特征向量的聚类的缺陷,设计了简单有效的聚类算法得到了比较理想的效果.     

基于服务分组的语义Web服务发现研究

提出一种多本体环境下的服务组织和发现方法。服务注册时,根据服务本体合理组织服务,即计算服务描述与本体概念组间的相似度,对注册服务进行分组。服务发现时,不需要将服务请求与所有注册的服务顺序地、一个一个地进行匹配判断。它首先根据服务请求的服务领域,缩小服务查找空间,然后根据服务请求概念集与本体概念组之间的相似度,进一步减小服务查找空间,进而提高了服务的发现效率。同时设定服务请求概念集与本体概念组之间的相似度阀值ξ的大小,来保证服务的查全率;而服务查找空间确定后,按照服务功能参数匹配筛选满足服务请求功能需求的服务,最后参考服务质量进行选择服务,提高服务查准率。     

An embedded system for real-time facial expression recognition based on the extension theory

This paper presents a novel facial expression recognition scheme based on extension theory. The facial region is detected and segmented by using feature invariant approaches. Accurate positions of the lips are then extracted as the features of a face. Next, based on the extension theory, basic facial expressions are classified by evaluating the correlation functions among various lip types and positions of the corners of the mouth. Additionally, the proposed algorithm is implemented using the XScale PXA270 embedded system in order to achieve real-time recognition for various facial expressions. Experimental results demonstrate that the proposed scheme can recognize facial expressions precisely and efficiently.     

基于相似度的区间数排序

为设计一种简便而又科学的区间数排序方法,在综合考虑区间数的特点、提取区间数中的隐形数据、对数据进行处理等基础上,根据各个区间数与目标区间数相似度程度的不同,提出了一种新的对区间数进行排序的方法,并提出了这种排序方法的一些优良性质,如:序关系完全性、传递性、等价替换性、区间数长度相等且区间数的属性为效益型时中心越大越好、区间数中心重合时区间越小越好等,充分表明了这种方法的科学性与可行性。最后,通过一个算例说明这种简单、方便、科学的排序方法的操作过程。     

An interval centroid based spread spectrum watermarking scheme for multi-flow traceback

Network flow watermarking schemes have been proposed to trace network attacks in the presence of stepping stones or anonymized channels. Most existing interval-based watermarking schemes are not only ineffective in tracing multiple network flows in parallel due to severe inter-flow interference in practice but also vulnerable to the newly introduced watermarking attacks. By combining the Interval Centroid Based Watermarking (ICBW) modulation approach with the Spread Spectrum (SS) watermarking coding technique, we herein propose an Interval Centroid Based Spread Spectrum Watermarking scheme (ICBSSW) for efficiently tracing multiple network flows in parallel. Based on our proposed theoretical model, a statistical analysis of ICBSSW, with no assumptions or limitations concerning the distribution of packet times, proves its effectiveness and robustness against inter-flow interference. ICBSSW can thwart multi-flow attacks by utilizing multiple Pseudo-Noise (PN) codes as random seeds for randomizing the location of the embedded watermark across multiple flows. The experiments using a large number of synthetically generated secure shell (SSH) traffic flows demonstrate that ICBSSW can efficiently trace multiple flows simultaneously and achieve robustness against inter-flow interference. Furthermore, our approach can be applied to other interval-based flow watermarking schemes besides ICBW for effective and efficient multi-flow traceback.     

An effort prediction interval approach based on the empirical distribution of previous estimation accuracy

When estimating software development effort, it may be useful to describe the uncertainty of the estimate through an effort prediction interval (PI). An effort PI consists of a minimum and a maximum effort value and a confidence level. We introduce and evaluate a software development effort PI approach that is based on the assumption that the estimation accuracy of earlier software projects predicts the effort PIs of new projects. First, we demonstrate the applicability and different variants of the approach on a data set of 145 software development tasks. Then, we experimentally compare the performance of one variant of the approach with human (software professionals') judgment and regression analysis-based effort PIs on a data set of 15 development tasks. Finally, based on the experiment and analytical considerations, we discuss when to base effort PIs on human judgment, regression analysis, or our approach.     

基于改进分组遗传算法的虚拟机放置研究

为了提高云计算的资源利用率以及减少能耗,采用改进的分组遗传算法来解决虚拟机放置的效率.通过对遗传算法的交配和突变等过程进行重新设计,提高遗传算法过程中优秀基因遗传给后代的几率,并提出了相应的算法,达到快速求解虚拟机放置问题的目的.实验结果表明,该算法可以快速采用最少的物理机来放置虚拟机,有效地提高了虚拟机放置问题的求解速度.