角色对象模式在电力监控系统中的应用

基于设计模式，角色对象模式是Composite模式和Decorate模式复合得到的新模式。角色对象模式将一个系统中的各级用户划分成不同对象，分别描述各对象的权限，并提供管理功能，从而降低程序耦合度，使系统结构清晰，易于维护。本文介绍了角色对象模式在电力监控系统中的应用。     

完备局部凸凹计数模式及其旋转不变纹理分类

局部凸凹模式是新近提出的一种有效纹理描述子,但对旋转纹理图像描述不足。针对局部凸凹模式算法的此缺陷,提出了局部凸凹计数模式进行旋转不变纹理描述。局部凸凹计数模式统计纹理的局部凸或凹模式数目,而抛弃了局部凸凹模式的结构信息。在纹理图像发生平面内旋转情况下,局部凸凹计数模式所提取的纹理特征具有不变性。另外,为了增强局部凸凹计数模式的性能,提出了完备局部凸凹计数模式算法。完备局部凸凹计数模式不仅统计了局部凸或凹模式数目,还对局部纹理的凸或凹强度进行了描述,中心像素所包含的鉴别信息也统计在完备局部凸凹模式算法中。二个纹理数据库和一个掌纹数据库上的试验充分表明完备局部凸凹计数模式是一种有效的旋转不变纹理特征提取算法。     

一种新的字符串单模式匹配算法

在分析了BM模式匹配算法的基础上,提出了一种新的字符串单模式匹配算法,该算法通过对模式中的字符进行等级划分,设置模式中各个字符的优先级,改进模式串的移动方式,减少了模式匹配的次数和字符比较的次数,有效的提高了模式匹配的效率。实验显示,该算法有效的提高了模式匹配的效率。     

在Visual Basic中实现观察者模式

设计模式在面向对象软件设计中应用广泛,遵循一定的编程模式才能使自己的代码便于理解,易于交流。介绍了一种常见的设计模式一一观察者模式,分析了在V isual Basic中实现观察者模式的可行性。Observer(观察者)模式是比较常用的一个模式,尤其在界面设计中应用广泛,文中通过样例提供了该模式的一个基本实现方法。     

模糊离散事件系统基于验证器的模式故障诊断

针对模糊系统在运行过程中可能出现由多个事件触发的故障,研究模糊离散事件系统模式故障的诊断问题,提出一种基于验证器的模式故障诊断方法.先对模糊离散事件系统中最常见的模式故障,引入S类型模式故障和T类型模式故障两个概念,再分别对模糊离散事件系统的S类型和T类型模式故障的可诊断性进行形式化.为验证模糊系统模式故障的可诊断性,构造一个验证器自动机,并得到一个关于模糊离散事件系统模式故障可诊断性的充分必要条件,实现对模糊系统模式故障的诊断.     

粒子群优化算法在点模式匹配中的应用

点模式匹配技术是计算机视觉和模式识别领域中的一个重要课题.将每个点模式编码为一个称为粒子的实值向量,并利用两幅图像的灰度矩阵来构造粒子的适应度函数,提出一种基于粒子群优化算法的点模式匹配新算法.系统初始化为一组随机解,通过迭代使粒子在解空间中追随当前较优的粒子进行搜索,从而找到最优解.仿真实验结果证明算法的有效性.     

A multiple case study of design pattern decay, grime, and rot in evolving software systems

Software designs decay as systems, uses, and operational environments evolve. Decay can involve the design patterns used to structure a system. Classes that participate in design pattern realizations accumulate grime—non-pattern-related code. Design pattern realizations can also rot, when changes break the structural or functional integrity of a design pattern. Design pattern rot can prevent a pattern realization from fulfilling its responsibilities, and thus represents a fault. Grime buildup does not break the structural integrity of a pattern but can reduce system testability and adaptability. This research examined the extent to which software designs actually decay, rot, and accumulate grime by studying the aging of design patterns in three successful object-oriented systems. We generated UML models from the three implementations and employed a multiple case study methodology to analyze the evolution of the designs. We found no evidence of design pattern rot in these systems. However, we found considerable evidence of pattern decay due to grime. Dependencies between design pattern components increased without regard for pattern intent, reducing pattern modularity, and decreasing testability and adaptability. The study of decay and grime showed that the grime that builds up around design patterns is mostly due to increases in coupling.     

A recursive patterns matching model for the dynamic pattern recognition problem

This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition problem. Dynamic patterns are characterized by having properties that change in intervals of time, such as a pedestrian walking or a car running (the negation of a dynamic pattern is a static pattern). Novel contributions of this paper include: (1) Formally develop the concepts of dynamic and static pattern, (2) design a recursive pattern matching model, which exploits the idea of recursivity and time series in the recognition process, and the unbundling/integration of pattern to recognize, and (3) develop strategies of pattern matching from two major orientations: recognition of dynamic patterns oriented by characteristic, or oriented by perception. The model is instantiated in several cases, to analyze its performance.     

精简高效用模式挖掘综述

全集高效用模式挖掘算法存在的关键问题之一是会产生冗余的高效用项集,这将导致用户很难在大量的高效用项集中发现有用的信息,严重降低了高效用模式挖掘算法的性能。为解决这一问题,衍生出了精简高效用模式挖掘算法,其主要包括最大高效用模式、闭合高效用模式、top-k高效用模式以及三者之间的组合高效用模式挖掘算法等。首先,介绍了精简高效用模式的相关问题描述;然后,从有无候选项集生成、一两阶段挖掘方法、数据结构类型和剪枝策略等角度,重点分类总结了精简高效用模式挖掘方法;最后,给出了精简高效用模式的进一步研究方向,包括处理基于负项的高效用精简模式、处理基于时间的高效用精简模式及处理动态复杂的数据等。     

动态空间网络中的黑洞模式挖掘算法

黑洞模式是人类移动模式研究中的标志性成果,但在移动模式的演化建模方面存在局限性,因此研究具有时间演化特性的黑洞模式。新模式定义需要满足群体规模性、空间区域性和时间持续性3方面要求。提出具有时间演化特性的动态空间网络模型,基于此模型定义新的黑洞模式,并提出相应的挖掘算法。为了提升模式挖掘算法的效率,设计了基于时空划分的候选模式剪枝算法,有效降低了挖掘算法在时空维中的搜索代价。最后,基于真实数据的实验结果表明了该黑洞模式及其挖掘算法的有效性和可行性。     

一种基于平面对称群的对称图案生成方法

织物图案中包含着各类蜿蜒曲线、规则与不规则的复杂元素。虽然剪纸图案和分 形图案的生成方法已有大量研究工作,但是面向新疆织物图案特点的计算机自动生成方法仍然 存在许多挑战性难题。针对新疆织物图案风格化建模难度大的问题,提出图案对称性作为图案 生成关键指标,构造循环群和二面体群等变映射与不变映射,研究图案对称性的表征方式,利 用对称图案探讨群的映射空间与对称图形之间的关联。通过群映射的仿真实验,绘制混沌吸引 子得到群对称图案,有效提高图案的对称性,丰富织物图案的花型设计。     

设计模式在电子商城中的研究与应用

以电子商城的开发为案例探讨了设计模式,并在表示层、业务层、数据访问层三个层次结构上分层使用设计模式。在系统开发中运用了多种重要的核心模式,比如MVC模式、观测者模式、策略模式、代理模式、DAO模式、外观模式、单例模式、简单工厂模式,并给出模式的实现。相对于传统的应用系统,该系统合理使用设计模式使得其结构严密、层次分明,具有更好的可重用性和可维护性。     

分布式随机离散事件系统的模式故障预测EI北大核心CSCD

本文以随机自动机为模型,研究分布式随机离散事件系统的模式故障预测问题.首先根据分布式随机离散事件系统的分布特性对模式故障协同可预测性概念进行形式化,并通过构造模式故障识别器来识别系统中发生的模式故障.然后,构造一个模式故障协同预测器,提出一种基于协同预测器的具有多项式复杂度的算法,得到关于分布式随机离散事件系统模式故障协同可预测性的充分必要条件,实现对分布式随机离散事件系统的模式故障预测.     

计算机支持的多用户协同图案设计系统模型

随着大型图案设计任务的复杂化、设计人员的专业化、设计地理位置的分散化以及计算机网络计算集成化,开发计算机支持的协同图案设计系统成为必要和可能。为了使人们对计算机协同设计系统有所了解,首先阐述了图案设计系统的基本过程,进而分析了计算机支持的多用户协同图案设计的基本要求;然后描述了时间、空间上分散,而在任务上又相互依赖的多个设计者之间的交互方式及协作机制;定义了协同图案设计系统的要素,并在此基础上,构造了多用户协同图案设计系统的逻辑模型;研究了协同编著系统中多用户图案的管理、操作和并发控制模型,同步图案设计的管理模型,控制模型以及消息的管理策略;最后设计了图案设计系统的结构模型。     

A New Two-Level Associative Memory for Efficient Pattern Restoration

Pattern reconstruction or pattern restoration in the presence of noise is a main problem in pattern recognition. An essential feature of the noise acting on a pattern is its local nature. If a pattern is split into enough sub-patterns, a few of them will be less or more affected by noise, others will remain intact. In this paper, we propose a simple but effective methodology that exploits this fact for the efficient restoration of a pattern. A pattern is restored if enough of its sub-patterns are also restored. Since several patterns can share the same sub-patterns, the final decision is accomplished by means of a voting mechanism. Before deciding if a sub-pattern belongs to a pattern, sub-pattern restoration in the presence of noise is done by an associative memory. Numerical and real examples are given to show the effectiveness of the proposal. Formal conditions under which the proposal guaranties perfect restoration of a pattern from an unaltered or and altered version of it are also given.     

Analogue pattern matching in a dendritic spine model based on phosphorylation of potassium channels

Modification of potassium channels by protein phosphorylation has been shown to play a role in learning and memory. If such memory storage machinery were part of dendritic spines, then a set of spines could act as an 'analogue pattern matching' device by learning a repeatedly presented pattern of synaptic activation. In this study, the plausibility of such analogue pattern matching is investigated in a detailed circuit model of a set of spines attached to a dendritic branch. Each spine head contains an AMPA synaptic channel in parallel with a calcium-dependent potassium channel whose sensitivity depends on its phosphorylation state. Repeated presentation of synaptic activity results in calcium activation of protein kinases and subsequent channel phosphorylation. Simulations demonstrate that signal strength is greatest when the synaptic input pattern is equal to the previously learned pattern, and smaller when components of the synaptic input pattern are either smaller or larger than corresponding components of the previously learned pattern. Therefore, our results indicate that dendritic spines may act as an analogue pattern matching device, and suggest that modulation of potassium channels by protein kinases may mediate neuronal pattern recognition.     

Polynomial-time inference of arbitrary pattern languages

A pattern is a finite string of constants and variables (cf. [1]). The language of a pattern is the set of all strings which can be obtained by substituting non-null strings of constants for the variables of the pattern. In the present paper, we consider the problem of learning pattern languages from examples. As a main result we present an inconsistent polynomial-time algorithm which identifies every pattern language in the limit. Furthermore, we investigate inference of arbitrary pattern languages within the framework of learning from good examples. Finally, we show that every pattern language can be identified in polynomial time from polynomially many disjointness queries, only.