基于GIS的网络拓扑构建与展示技术方法研究

随着计算机和互联网技术的快速发展,计算机网络规模趋于庞大,结构趋于复杂。为了简洁、高效、直观、全面地展示网络拓扑信息,本文对基于地理信息的网络拓扑构建及展示技术进行研究,构建用于复杂网络多样化展示的多视图模型,并在网络拓扑可视化中使用细节层次方法,让管理和决策人员可以快速地获得包括网络地理信息、设备信息、其他网络状态信息在内的网络拓扑多方位信息,为网络管理工作提供支撑。     

启发优化平衡的网络拓扑模型

网络拓扑模型广泛地应用于网络研究中。幂法则提出以后,出现了一些符合幂法则的网络拓扑模型,但是这些模型没有充分考虑到底层节点的几何排列,针对这种情况,提出了一种改进的“启发式最优化平衡”网络拓扑模型,生成的拓扑图符合幂法则规律,且具有适当数量的叶子节点,更加接近互联网真实特征。     

基于地理信息图的网络拓扑显示

在网络管理中引入WebGIS可以实现在地理信息图上显示网络拓扑。该文介绍拓扑发现方法、空间数据处理模型和拓扑数据处理模型,实现基于地理信息图的网络拓扑显示系统。该系统使用SVG文档显示空间数据,描述网络中各种资源的真实地理位置,为网络管理提供了一种新工具。     

一种遵循Rich-Club现象的网络拓扑模型

基于现有的AS网络拓扑生成模型,本文提出了一个遵循Rich-Club现象的新网络拓扑模型。该模型不仅保正反映AS网络的基本性质,还能满足AS网络所有的特性Rich-Club现象。仿真结果表明,本文的模型和算法是有效的。     

复杂网络中即时通信蠕虫病毒传播的研究

即时通信网络是在实际的Internet网络拓扑之上构建一层虚拟的网络拓扑,它遵循幂率分布。针对复杂网络的特点,在对即时通信网络拓扑结构进行分析的基础上,提出了一种即时通信蠕虫病毒的群体生灭模型。该模型以分析蠕虫病毒群体的概率分布来演化即时通信网络上蠕虫病毒的传播规律。模型分析表明即时通信蠕虫病毒的群体生灭模型分析结果与无标度网络上病毒的传播特性具有较好的一致性。     

一种基于随机游走和共点的社交网络拓扑模型

研究移动社交网络拓扑模型,有助于在更深层次上理解移动社交网络的结构特性和进行相关安全软件的开发。根据移动社交网络的高度动态性,提出了基于随机游走的有向网络拓扑模型,通过上一状态的节点累计连接时间和共点个数变化来决定当前节点状态。仿真实验表明,构造的移动社交网络拓扑模型符合真实移动社交网络环境下具有幂律特性的拓扑结构。     

基于免疫对称网络的P2P网络拓扑结构

基于生物免疫系统的对称网络理论,设计了生物网络仿真平台上的对等(P2P)网络拓扑结构模型.该P2P网络的节点上的生物实体能够相互作用维持网络平衡与稳定,并向用户提供服务.在生物网络仿真平台上,通过构建P2P网络拓扑结构模型并进行仿真实验,验证了所设计的网络拓扑结构能有效地满足用户需求.     

基于簇域的P2P信誉评估模型

针对现有的信任模型的网络消耗大、信任数据存储和查询等问题,设计了一种基于簇域的P2P网络拓扑结构的信誉评估模型P2P-Crep.模型采用双层网络拓扑结构,解决信任数据的存储和查询及网络消耗问题.通过对信誉模型的性能分析和仿真试验,验证了模型的动态性、可扩展性以及对电子商务恶意行为的有效抵制.     

一种基于GIS的网络层次化地图模型及实现算法

大型计算机网络地域分布广,网元数目众多,传统网络管理系统以网络拓扑这种虚拟空间的方式实现网络的监控管理,没有充分利用网元的地理位置信息,网络监控和管理存在不便之处.地理信息系统(GIS)的应用能够赋予网络监控管理系统清晰直观、易于监控和管理的特性.通过建立一种基于GIS的网络层次化地图模型,系统解决了网络拓扑与GIS地图有效结合这一问题,实现了在GIS地图中网络拓扑与运行状态信息的层次化管理,给出了网络GIS地图的生成、维护的非递归算法.模型和算法的有效性和完备性在大型网络性能监测与分析系统NIPMAS中得到了实际验证.     

一种基于无标度局域世界演化网络模型的 无线传感器网络拓扑构建

无标度网络具有场景适应性强,且应对随机性打击效果较好等特点.为提高无线传感器网络的抗毁性能,从网络拓扑演化角度入手,构建具有无标度特性的网络拓扑.结合无线传感器网络中每个节点都有其通讯范围的特点,将无标度网络引入到无线传感器网络,且在择优演化过程中,各节点度不得超过设定值,构建基于无标度局域世界演化网络模型的无线传感器网络拓扑,并对模型进行仿真.从结果可明显看出,优化的模型中连边较短,即优化的模型平均最短路径降低,可有效减少数据收发过程中能量的消耗,延长网络生存周期,提高网络抗毁性.     

Composite performance and dependability analysis

Composite performance and dependability analysis is gaining importance in the design of complex, fault-tolerant systems. Markov reward models are most commonly used for this purpose. In this paper, an introduction to Markov reward models including solution techniques and application examples is presented. Extensions of Markov reward models to semi-Markov reward models are also mentioned. A brief discussion of how task completion time models and models of queues with breakdowns and repairs relate to Markov reward models is also given.     

Linear, Branching Time and Joint Closure Semantics for Temporal Logic

Temporal logic can be used to describe processes: their behaviour ischaracterized by a set of temporal models axiomatized by a temporaltheory. Two types of models are most often used for this purpose: linearand branching time models. In this paper a third approach, based onsocalled joint closure models, is studied using models which incorporateall possible behaviour in one model. Relations between this approach andthe other two are studied. In order to define constructions needed torelate branching time models, appropriate algebraic notions are defined(in a category theoretical manner) and exploited. In particular, thenotion of joint closure is used to construct one model subsuming a setof models. Using this universal algebraic construction we show that aset of linear models can be merged to a unique branching time model.Logical properties of the described algebraic constructions are studied.The proposed approach has been successfully aplied to obtain anappropriate semantics for non-monotonic reasoning processes based ondefault logic. References are discussed that show the details of theseapplications.     

Knowledge Engineering for Rough Sets Based Decision‐Making Models

In this paper, a review of decision‐making models based on the rough set theory is presented. The use of these techniques allows for the presence of uncertainty in computer models that are developed for decision making, and to formulate the decision‐making models using the experiences of previous decisions made. Since the formulation of these models differs from the classical approach of decision‐making models, in this paper, the models are analyzed and a method is proposed for its implementation.     

几种随机相互作用势相关的交通流模型比较

为了研究随机因素对交通流的影响,人们提出了几种考虑顾前相互作用势的交通流模型.它们均基于简单非对称排斥过程和微观动力学模型,对于描述交通流的随机演化行为具有优越性.本文对几种势相关的交通流模型进行比较,特别是比较不同模型的车辆状态迁移的概率计算机制;进而通过数值仿真,比较不同模型刻画的随机演化现象.通过本文的比较,得到几种随机相互作用势相关的交通流模型的流量-密度图、时空斑图以及三维演化结果.为进一步优化势函数结构,建立符合实际交通的随机交通流模型奠定基础.     

Machine intelligence-toward an artificial brain

An artificial brain project in which conceptual models are specified algorithmically is discussed. The feature that distinguishes the approach used in this project from connectionist neural models is its vertical abstraction of brain structure and function. The models comprise multiple local networks of neurons, each of which is a complex network of molecular and chemical processes. The memory manipulation system and reference neuron mechanism used in this approach are described. The development of simulation models for the different conceptual components and the assessment of these models are reviewed     

Designing business capability-aware configurable process models

Process Aware Information Systems manage processes within organisations on the basis of business process models. These models can be created either from scratch or by reusing exiting reference process models.Particular types of reference models are configurable process models that are created by merging multiple models into a single one that can be customized to the needs of the business experts. Using those models presents two main challenges: their creation and their configuration.In this paper, we focus on the first challenge and propose a novel algorithm for merging process models into a configurable process model. The difference in our work is the pre-annotated process models with their business capabilities that report on what actions each process element achieves. Our algorithm generates configurable models that are also annotated with their capabilities that can be used to face the second challenge of these models: the configuration phase.We tested our algorithm using real-world process models to evaluate the required creation time and resulting compression rate after merging the input models. The results show that the models can be created in few milliseconds and achieving a compression rate of 50%. We further carried out interviews with domain experts to assess the usefulness and the level of maturity of this work. The results show the importance of the automation of process merging using a tool support that we proposed. However, further adaptation efforts are required to integrate this work in the working environments of the interviewed experts.     

Improving the quality of use case models using antipatterns

Use case (UC) modeling is a popular requirements modeling technique. While these models are simple to create and read; this simplicity is often misconceived, leading practitioners to believe that creating high quality models is straightforward. Therefore, many low quality models that are inconsistent, incorrect, contain premature restrictive design decision and contain ambiguous information are produced. To combat this problem of creating low quality UC models, this paper presents a new technique that utilizes antipatterns as a mechanism for remedying quality problems in UC models. The technique, supported by the tool ARBIUM, provides a framework for developers to define antipatterns. The feasibility of the approach is demonstrated by applying it to a real-world system. The results indicate that applying the technique improves the overall quality and clarity of UC models.     

混合像元分解模型综述

介绍了五种混合像元分解模型，分别为线性模型、概率模型、几何光学模型、随机几何模型和模糊分析模型，并对其中几种常用模型的优缺点及其适用性进行总结讨论。并对不同模型之间的相似和差异性进行比较分析。     

An appropriateness framework for the Dutch Meuse decision support system

Models are essential in a decision support system for river basin management. In a decision support system for integrated planning and management, the use of appropriate models is important to avoid models being either too simple or too complex. In this paper, appropriate models refer to models that are good-enough-but-not-more-than-that to obtain an acceptable ranking of river engineering measures under uncertainty. A systematic approach called ‘appropriateness framework’ is proposed to determine appropriate models that can be used in a decision support system. The approach is applied to a decision support system for the Dutch Meuse River. One important component of this decision support system, flood safety, is used in this paper to demonstrate how this approach works. The results show that the approach is very useful in helping to determine appropriate models. Potential applications of the approach in other decision support systems are discussed. The approach presented in this paper is designed as a tool to stimulate the communication between decision makers and modelers and to promote the use of models in decision-making for river basin management.     

Barrier certificates for nonlinear model validation

Methods for model validation of continuous-time nonlinear systems with uncertain parameters are presented in this paper. The methods employ functions of state-parameter-time, termed barrier certificates, whose existence proves that a model and a feasible parameter set are inconsistent with some time-domain experimental data. A very large class of models can be treated within this framework; this includes differential-algebraic models, models with memoryless/dynamic uncertainties, and hybrid models. Construction of barrier certificates can be performed by convex optimization, utilizing recent results on the sum of squares decomposition of multivariate polynomials.