动态分类的UML表示方法

动态分类是指对象所属的类可随时间而变化。在进行系统分析和软件开发中,需要真实地描述研究对象在其生命周期中的变化,动态分类正是这样一种机制。但是由于许多面向对象程序设计语言都不支持动态分类,因此需要用别的方法来描述动态分类,文中给出了用ＵＭＬ表达动态分类的四种机制,并对这四种机制进行了比较。     

一种有效的用于数据挖掘的动态概念聚类算法

概念聚类适用于领域知识不完整或领域知识缺乏时的数据挖掘任务.定义了一种基于语义的距离判定函数,结合领域知识对连续属性值进行概念化处理,对于用分类属性和数值属性混合描述数据对象的情况,提出了一种动态概念聚类算法DDCA(domain-baseddynamicclusteringalgorithm).该算法能够自动确定聚类数目,依据聚类内部属性值的频繁程度修正聚类中心,通过概念归纳处理,用概念合取表达式解释聚类输出.研究表明,基于语义距离判定函数和基于领域知识的动态概念聚类的算法DDCA是有效的.     

系统故障演化过程描述方法研究

为了解系统故障演化过程,提供可行的描述方法,进一步对空间故障网络理论进行研究。将原有空间故障网络结构◢W=（V,L,R,H,B）改为W=（O,S,L,X）,即用四要素对象、状态、连接和因素描述故障演化过程构建空间故障网络。提出枚举法和实例法具体进行故障演化过程描述。研究三级往复式压缩机的第一级故障过程,并辨识了事件的对象、对象的状态及事件的逻辑关系,绘制了空间故障网络。以V▼10▽和V▼2▽为例得到了转换的空间故障树,并以V▼2▽为例研究了故障演化过程。得到了127种故障模式,其中64种有利于V▼2▽◣发生,剩余63种不利于发生。根据演化过程描述需要将故障演化过程进一步分类,论述了故障演化机理。     

对象的动态组装及其实现机制

提出并讨论了对象动态组装概念及其实现机制。在给出对象动态组装的模型之后，对其语言描述、实现、运行机制作了阐述。对象动态组装是软件重用的高级阶段。作为对象描述和对象作用的机制，它为软件集成块的实现提出了新思想。     

基于对象代理数据库的微生物信息服务系统

提出了一种采用对象代理数据库实现微生物数据管理的新方法。该方法将微生物菌种资源数据的共性描述信息定义为基本微生物对象,其代理对象可以用来表示微生物菌种资源分类的多样性,并定义不同菌种的特性性状信息,也可以用来定义微生物资源不同类别的扩展关联信息;通过对象更新迁移可以支持数据动态分类,利用对象视图机制可以支持模式演化,跨类查询机制则实现了高效的数据检索;实现了一个基于对象代理数据库的微生物信息服务系统。实验测试表明,该方法比传统方法更有效。     

知识融合中的反馈评估和参数校正的机制

提出了一种在知识融合框架结构中建立根据应用结果进行反馈评估和参数校正的机制.相关的知识资源对象和应用需求描述通过本体论方法被表示为知识空间中的对象节点;根据知识融合的特点建立了知识对象的评价参数体系,提出了基于反馈和参数校正机制的自适应模块;分析了该模块与其他模块之间的层次关系,仿真实验说明了该机制的合理性.     

一种基于静态和动态信任的普适环境信任模型

针对普适环境中现有信任模型对信任证据的考虑存在片面性的问题,在分析四种信任证据的基础上,将信任分为静态和动态两种类型,并提出了一种基于该分类方法的信任模型,描述了该模型的工作机制。该模型克服了对信任证据考虑不完整的缺陷,有利于在普适实体之间建立起可靠的信任关系,提高系统的安全性。     

知识流的Petri网模型

Petri网是一种用以刻画通信机制的建模工具,在流程控制上得到了广泛应用。知识管理作为一种新的管理理论,其研究尚停留在对基本概念的剖析和激励方式的探索上。知识管理的对象是“知识”,对知识流动机制的刻画能够更好地描述企业的知识资产分布情况,方便监控并进行知识管理。用Petri网作为工具对知识的流动进行建模,从而得到知识流的概念模型。解决了对知识流动的共享性、并行性、复杂性、分布性等特性的刻画,为知识管理提供了知识流的建模工具,并为进一步对知识流实施监控提供了可能性。最后对知识流模型进行了应用研究,对Petri网对于知识流的建模能力予以说明。     

用于描述面向对象并发系统的实用图文法模型*

面向对象的并发系统与传统的并发系统(如用CSP或CCS所描述的系统)的不同之处在于其进程结构的动态性，系统中的进程以及进程之间的通信链路随着对象的变化而动态地建立或撤消．图文法模型比其他形式化工具更适合描述这种并发和动态的特性．这里我们介绍一个新的用于描述面向对象并发系统的图文法模型，在该模型中为系统的设计开发者提供了一个用来描述系统的静态和动态结构的语言工具——CSDL语言．在面向对象并发系统开发支持环境 的支持下，用CSDL语言描述的面向对象并发系统的结构可以转换成对系统运行期进程互联结构的控制机制，     

新的面向对象知识表示方法

针对传统知识表示方法缺乏过程性知识表示能力、不具备动态特征、难以表示装备保障领域知识的问题,在借鉴框架表示法、语义网络表示法的基础上,结合面向对象思想,提出了一种新的知识表示方法——动态对象网络(DON)。DON表示法以类来表示概念,将知识表示为概念以及概念间的关系。给出了概念的详细结构及其演化机制,关系的描述方式,并将守护方法思想引入概念中,在此基础上探讨了DON中的推理机制,为灵活、动态表示装备保障领域知识,实现装备保障信息的共享和融合提供了基础。     

Optimal ABC inventory classification using interval programming

Inventory classification is one of the most important activities in inventory management, whereby inventories are classified into three or more classes. Several inventory classifications have been proposed in the literature, almost all of which have two main shortcomings in common. That is, the previous methods mainly rely on an expert opinion to derive the importance of the classification criteria which results in subjective classification, and they need precise item parameters before implementing the classification. While the problem has been predominantly considered as a multi-criteria, we examine the problem from a different perspective, proposing a novel optimisation model for ABC inventory classification in the form of an interval programming problem. The proposed interval programming model has two important features compared to the existing methods: it provides optimal results instead of an expert-based classification and it does not require precise values of item parameters, which are not almost always available before classification. Finally, by illustrating the proposed classification model in the form of numerical example, conclusion and suggestions for future works are presented.     

Efficient sparse nonparallel support vector machines for classification

In this paper, we propose a novel nonparallel classifier, named sparse nonparallel support vector machine (SNSVM), for binary classification. Different with the existing nonparallel classifiers, such as the twin support vector machines (TWSVMs), SNSVM has several advantages: It constructs two convex quadratic programming problems for both linear and nonlinear cases, which can be solved efficiently by successive overrelaxation technique; it does not need to compute the inverse matrices any more before training; it has the similar sparseness with standard SVMs; it degenerates to the TWSVMs when the parameters are appropriately chosen. Therefore, SNSVM is certainly superior to them theoretically. Experimental results on lots of data sets show the effectiveness of our method in both sparseness and classification accuracy and, therefore, confirm the above conclusions further.     

基于原型超平面的多类最接近支持向量机

基于广义特征值的最接近支持向量机（proximal support vector machine via generalized eigenvalues,GEPSVM）摒弃了传统意义下支持向量机典型平面的平行约束,代之以通过优化使每类原型平面尽可能接近本类样本,同时尽可能远离它类样本的准则来解析获得原型平面;从而避免了SVM的二次规划,其分类性能达到甚至超过了SVM．但GEPSVM仍存在如下不足：①仅对两分类问题而提出,无法直接求解多分类问题;②存在正则化因子的选择问题;③求解原型平面的广义特征值问题中所涉及的矩阵一般仅为半正定,容易导致奇异性问题．通过定义新的准则,构建了一个能直接求解多个原型超平面的多分类方法,称之为基于原型超平面的多类最接近支持向量机,较之GEPSVM,该方法优势在于：①无正则化因子选择的困扰;②可同时求解多个超平面,对两分类问题,分类性能达到甚至优于GEPSVM;③超平面的选择问题转化为简单特征值而非广义特征值求解问题;④原型平面的选择只依赖于本类样本,故不必考虑多分类情形时的数据不平衡问题．     

基于Web Services的构件分类与检索研究

目前有很多构件分类和检索方法,其中大部分都不能很好地支持构件库的跨平台操作。文中介绍了这种基于WebServices的构件分类检索的模型,在以刻面分类检索的基础上采用Web Services技术对其封装,只要访问方能遵照相应WebServices接口的定义来发送和接收消息即可,实现了构件库跨平台、跨语言的互操作功能,大大提高了构件库的普适性、可互操作性和可扩展性。     

A new mathematical programming approach to multi-group classification problems

In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.     

A practical approach to programming with assertions

Embedded assertions have been recognized as a potentially powerful tool for automatic runtime detection of software faults during debugging, testing, maintenance and even production versions of software systems. Yet despite the richness of the notations and the maturity of the techniques and tools that have been developed for programming with assertions, assertions are a development tool that has seen little widespread use in practice. The main reasons seem to be that (1) previous assertion processing tools did not integrate easily with existing programming environments, and (2) it is not well understood what kinds of assertions are most effective at detecting software faults. This paper describes experience using an assertion processing tool that was built to address the concerns of ease-of-use and effectiveness. The tool is called APP, an Annotation PreProcessor for C programs developed in UNIX-based development environments, APP has been used in the development of a variety of software systems over the past five years. Based-on this experience, the paper presents a classification of the assertions that were most effective at detecting faults. While the assertions that are described guard against many common kinds of faults and errors, the very commonness of such faults demonstrates the need for an explicit, high-level, automatically checkable specification of required behavior. It is hoped that the classification presented in this paper will prove to be a useful first step in developing a method of programming with assertions     

模糊线性支持向量回归机

该文研究了当训练点的输出为三角模糊数时,支持向量回归机的构建问题。首先将模糊回归问题转化为模糊分类问题,并将求模糊最优分类超平面问题转化为求解带有模糊决策的机会约束规划问题。利用基于模糊模拟的遗传算法求解带有模糊决策的机会约束规划,得到模糊最优分类超平面(模糊方程),解模糊方程得到模糊回归函数。在此基础上,得出模糊线性支持向量回归机(算法)。从而较好地解决了支持向量机中含有模糊信息的模糊回归问题。最后,给出显示模糊线性支持向量回归机特点的模糊支持向量集的定义。     

分布式环境软件开发平台

对一般的软件开发人员编写分布式并行计算程序是比较艰难的。它要求设计者对任务的分解、分配及子任务间的交互等问题有更高的技术。本文给出了分布式环境并行计算程序开发平台的一种新构架,研制了一个网络环境的可视化并行程序设计平台。用户只须将并行问题可视化地以图形方式描述出来,任务的调度、任务间通信都由系统自动完成
成,因而大大地方便了用户进行并行程序设计。     

Self-Universum support vector machine

In this paper, for an improved twin support vector machine (TWSVM), we give it a theoretical explanation based on the concept of Universum and then name it Self-Universum support vector machine (SUSVM). For the binary classification problem, SUSVM takes the positive class and negative class as Universum separately to construct two classification problems with Universum; therefore, two nonparallel hyperplanes are derived. SUSVM has several improved advantages compared with TWSVMs. Furthermore, we improve SUSVM by formulating it as a pair of linear programming problems instead of quadratic programming problems (QPPs), which leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on several benchmark datasets.     

遗传程序设计用于金属间化合物三元填隙D88结构形成条件的判别

本文将遗传程序设计用于金属间化合物三元填隙Ｄ８８结构形成条件的判别．对６５种化合物的研究结果表明，该方法性能良好，可望成为化合物结构分析的辅助手段．