基于PVS的UML类图和序列图的一致性检验

针对UML类图和序列图的一致性问题,在充分考虑了类继承关系、关联关系、类方法的可见性以及类方法的前、后置条件等因素对一致性影响的基础上,给出了判定类图和序列图一致性的必要条件和PVS元理论,提出了一种基于定理证明器PVS的一致性检验方法.在检验UML模型一致性时,把一致性检验问题转化为逻辑定理证明问题.实践表明,该方法对于提高UML模型的可信度,减少系统实现阶段的错误起到了一定作用.     

面向对象技术在流域洪水预报系统中的应用

通过对现有的洪水预报系统的分析,确定了复用率低以及难于扩展是目前洪水预报应用系统在实现方面存在的普遍问题。利用面向对象的Java语言作为开发工具,进行洪水预报应用系统的开发,使系统具有较强的可扩展性和复用能力,能有效地缩短新系统的开发周期,因而是进行洪水预报系统开发的有效工具。     

A Chinese Web page clustering algorithm based on the suffix tree

In this paper, an improved algorithm, named STC-I. is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction policy. The experimental results show that the new algorithm keeps advantages of STC, and is better than STC in precision and speed when they are used to cluster Chinese Web page.     

CFSBC： Clustering in High-Dimensional Space Based on Closed Frequent Item Set

Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Many clustering algorithms are not applicable to high dimensional space for its sparseness and decline properties. Dimensionality reduction is an effective method to solve this problem. The paper proposes a novel clustering algorithm CFSBC based onclosed frequent hemsets derived from association rule mining. which can get the clustering attributes with high efficiency. The algorithm has several advantages. First, it deals effectively with the problem of dimensionality reduction. Second, it is applicable to different kinds of attributes, Third, it is suitable for very large data sets. Experiment shows that the proposed algorithm is effective and efficient     

The refinement algorithm consideration in text clustering scheme based on multilevel graph

To construct a high efficient text clustering algorithm, the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed, The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application, The experiment result demonstrated that the muhilevel graph text clustering algorithm is available,     

Study on properties of residue-residue contacts in protein

Residue-residue contacts are very important in forming protein structure. In this work, we calculated the average probability of residue-residue contacts in 470 globular proteins and analyzed the distribution of contacts in the different interval of residues using Contacts of Structural Units (CSU) and Structural Classification (SCOP) software. It was found that the relationship between the average probability PL and the residue distance L for four structural classes of proteins could be expressed as lgPL=a+b x L, where a and b are coefficients. We also discussed the connection between two aspects of proteins which have equal array residue number and found that the distribution probability was stable (or un-stable) if the proteins had the same (or different) compact (for example synthase) in the same structural class.     

A minimal axiom group for rough set based on quasi-ordering

Rough set axiomatization is one aspect of rough set study to characterize rough set theory using dependable and minimal axiom groups. Thus, rough set theory can be studied by logic and axiom system methods. The classic rough set theory is based on equivalent relation, but rough set theory based on reflexive and transitive relation (called quasi-ordering) has wide applications in the real world. To characterize topological rough set theory, an axiom group named RT, consisting of 4 axioms, is proposed. It is proved that the axiom group reliability in characterizing rough set theory based on similar relation is reasonable. Simultaneously, the minimization of the axiom group, which requires that each axiom is an equation and each is independent, is proved. The axiom group is helpful for researching rough set theory by logic and axiom system methods.     

Microbial control of diamondback moth, Plutella xylostella L. （Lepidoptera： Yponomeutidae） using bacteria （Xenorhabdus nematophila） and its metabolites from the entomopathogenic nematode Steinernema carpocapsae

Cells and cell-free solutions of the culture filtrate of the bacterial symbiont, Xenorhabdus nematophila taken from the entomopathogenic nematode Steinernema carpocapsae in aqueous broth suspensions were lethal to larvae of the diamondback moth Plutella xylostella. Their application on leaves of Chinese cabbage indicated that the cells can penetrate into the insects in the absence of the nematode vector. Cell-free solutions containing metabolites were also proved as effective as bacterial cells suspension. The application of aqueous suspensions of cells of X.nematophila or solutions containing its toxic metabolites to the leaves represents a possible new strategy for controlling insect pests on foliage.     

厄米-高斯光束倾斜入射类透镜介质的近轴传输

将描述光在类透镜介质中传播的Helmholtz方程化为具有线性谐振子势的Schr dinger方程形式,借助于量子力学中的"含时"么正变换技术,解析求解光束倾斜入射到类透镜介质中传输时的场分布,得到场传输的传播核因子,研究任意倾斜入射光束位形的传输问题,并对厄米 高斯光束的传输进行计算.研究结果表明:厄米 高斯光束倾斜入射进类透镜介质束宽度的演变与光束正入射的情况一样,随传输距离作周期性变化,但因光束倾斜入射,厄米 高斯光束的传输出现了新效应,即光强分布中心不再沿直线,传播方向也不断变化,出现与横坐标相关的附加局域相因子.     

A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA

Most of the earlier work on clustering mainly focused on numeric data whose inherent geometric properties can be exploited to naturally define distance functions between data points. However, data mining applications frequently involve many datasets that also consists of mixed numeric and categorical attributes. In this paper we present a clustering algorithm which is based on the k-means algorithm. The algorithm clusters objects with numeric and categorical attributes in a way similar to k-means. The object similarity measure is derived from both numeric and categorical attributes. When applied to numeric data, the algorithm is identical to the k-means. The main result of this paper is to provide a method to update the “cluster centers“ of clustering objects described by mixed numeric and categorical attributes in the clustering process to minimise the clustering cost function. The clustering performance of the algorithm is demonstrated with the two well known data sets, namely credit approval and abalone databases.