Some new error inequalities for a generalized quadrature rule of open type

Some new error inequalities for a generalized quadrature rule of open type are established. Especially, two sharp inequalities are derived when n is an odd and an even integer, respectively.     

基于OWL本体的UML类图推理

分析UML模型一致性检测的研究意义,以及UML类图推理的研究现状,提出一种基于OWL本体的UML类图推理方案.研究UML类图向OWL本体转换的映射规则,给出UML类图包含关系、可满足性、等价关系和相离关系的判断方法及其推理规则.利用Jena推理机制实现了UML类图包含关系、可满足性、等价关系和相离关系的推理.     

基于参考点和投票规则的多目标粒子群优化群体决策

针对巨量可选方案的群体决策问题,提出了一个新的基于参考点和投票规则的多目标粒子群优化算法。该算法把个体与参考点的支配关系或者距离作为一个重要因素,在选择引导者的锦标赛方法,局部最优更新规则,以及外部种群档案剪枝规则中都嵌入了基于支配关系或距离因素的投票规则,以找到群体决策解,并且提高搜索效率。仿真结果表明该算法有效。     

Queueing analysis and optimal control of and systems

We first consider a finite-buffer single server queue where arrivals occur according to batch Markovian arrival process (BMAP). The server serves customers in batches of maximum size ‘b’ with a minimum threshold size ‘a’. The service time of each batch follows general distribution independent of each other as well as the arrival process. We obtain queue length distributions at various epochs such as, pre-arrival, arbitrary, departure, etc. Some important performance measures, like mean queue length, mean waiting time, probability of blocking, etc. have been obtained. Total expected cost function per unit time is also derived to determine the optimal value N^* of N at a minimum cost for given values of a and b. Secondly, we consider a finite-buffer single server queue where arrivals occur according to BMAP and service process in this case follows a non-renewal one, namely, Markovian service process (MSP). Server serves customers according to general bulk service rule as described above. We derive queue length distributions and important performance measures as above. Such queueing systems find applications in the performance analysis of communication, manufacturing and transportation systems.     

Hypothesis testing in rank-size rule regression

This note examines testing methods for Paretoness in the framework of rank-size rule regression. Rank-size rule regression describes a relationship found in the analysis of various topics such as city population, words in texts, scale of companies and so on. In terms of city population, it is basically an empirical rule that log?(_S(i))$\log ?(S_{(i)})$ is approximately a linear function of log?(i)$\log ?(i)$ where _S(i)$S_{(i)}$ is the number of population of i  th largest city in a country. This is closely related to the so-called Zipf’s law. It is known that this kind of empirical observation is found when the city population is a random variable following a Pareto distribution. Thus one may be willing to test if city size has a Pareto distribution or not. Rosen and Resnick [K.T. Rosen, M. Resnick, The size distribution of cities: an explanation of the Pareto law and primacy, Journal of Urban Economics 8 (1980), 165–186] and Soo [K.T. Soo, Zipf’s law for cities: a cross country investigation, Regional Science and Urban Economics (35) 2005, 239–263] regress log?(_S(i))$\log ?(S_{(i)})$ on log?(i)$\log ?(i)$ and log?²(i)${\log ?}^2(i)$ and test the null of Paretoness by standard t-test for the latter regressor. It is found that t-statistics take large values and the Paretoness is rejected in many countries. We study the statistical properties of the t-statistic and show that it explodes asymptotically, in fact, by simulation and thus the t-test does not provide a reasonable testing procedure. We propose an alternative test statistic which seems to be asymptotically normally distributed. We also propose a test with the null hypothesis that the city size distribution is Pareto with exponent unity, which is a modification of the F-test.     

Efficient single-pass frequent pattern mining using a prefix-tree

The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern mining because it can dramatically improve performance compared to the candidate generation-and-test paradigm of Apriori. However, it still requires two database scans, which are not consistent with efficient data stream processing. In this paper, we present a novel tree structure, called CP-tree (compact pattern tree), that captures database information with one scan (insertion phase) and provides the same mining performance as the FP-growth method (restructuring phase). The CP-tree introduces the concept of dynamic tree restructuring to produce a highly compact frequency-descending tree structure at runtime. An efficient tree restructuring method, called the branch sorting method, that restructures a prefix-tree branch-by-branch, is also proposed in this paper. Moreover, the CP-tree provides full functionality for interactive and incremental mining. Extensive experimental results show that the CP-tree is efficient for frequent pattern mining, interactive, and incremental mining with a single database scan.     

Applying a novel decision rule to the sphere-structured support vector machines algorithm

The traditional sphere-structured support vector machines algorithm is one of the learning methods. It can partition the training samples space by means of constructing the spheres with the minimum volume covering all training samples of each pattern class in high-dimensional feature space. However, the decision rule of the traditional sphere-structured support vector machines cannot assign ambiguous sample points such as some encircled by more than two spheres to valid class labels. Therefore, the traditional sphere-structured support vector machines is insufficient for obtaining the better classification performance. In this article, we propose a novel decision rule applied to the traditional sphere-structured support vector machines. This new decision rule significantly improves the performance of labeling ambiguous points. Experimental results of seven real datasets show the traditional sphere-structured support vector machines based on this new decision rule can not only acquire the better classification accuracies than the traditional sphere-structured support vector machines but also achieve the comparable performance to the classical support vector machines. An erratum to this article can be found at     

一种粗糙集属性约简算法

基于粗糙集理论提出一种新的属性重要度的度量方法,引入决策强度的概念,克服经典粗糙集理论约简定义的不完备性及无法获得最优属性约简的缺陷,改进基于信息熵的启发式属性约简算法,通过对既有线CTCS-2级车站列控中心软件测试平台的测试数据的实证分析,成功获得最优属性约简,发现数据之间的潜在联系及规律,给出决策规则,使决策分析更为高效。     

一致性测试的静态需求测试研究

针对一致性测试中的静态需求测试通常采用手工方法而产生的效率低问题,提出一种基于统计学习的方法,包括静态需求的关联规则表示,并对采样的协议数据进行关联规则挖掘,如果得到的规则与需求规则一致,静态需求就可以得到统计上的测试检验。该方法在OSPFv3协议测试中取得了较好的效果。     

多数据库中的负关联规则挖掘技术及发展趋势

负关联规则反映了数据项之间的互斥关系,能提供很多有用的信息,在决策支持中起重要作用,但现行的挖掘算法主要是针对单一数据库的挖掘,多数据库中负关联规则的挖掘还未引起重视。该文介绍负关联规则的研究现状、主要挖掘方法以及冗余正负关联规则的修剪方法,对多数据库中关联规则挖掘研究现状和主要技术进行论述,并展望多数据库中负关联规则挖掘的发展趋势。