无线衰落信道的Markov模型研究

高速移动数据通信是目前通信的发展热点之一 ,而当数据传输速率较高时 ,无线信道中相邻数据分组之间的相关性不容忽视 ,更接近这种记忆信道的一种模型是Markov模型。本文研究了瑞利衰落信道的一阶Markov模型。结果表明 ,采用一阶的Markov信道模型已经足以得到令人满意的效果 ,没有必要采用复杂的高阶模型 ,而且利用该模型可以简化对高层协议性能的研究。     

基于图像高阶MARKOV链模型的扩频隐写分析

 扩频隐写分析是信息隐藏研究领域的一个重要方面.文章提出基于高阶Markov链的数字图像统计分布模型,在对常用图像扫描方法构成高阶Markov链的效果进行比较后,采用Hilbert扫描方式构建数字图像n阶Markov链模型,进而提出度量数字图像隐写统计安全性的n阶Markov链测度,并证明其有界.最后文章通过研究扩频隐写对高阶Markov链模型经验矩阵的影响,利用该模型提取图像统计特征,并使用支持向量机对几种常用图像扩频隐写方法进行分析.实验说明文章所提方法对扩频隐写分析效果良好,且随着模型阶数提高,分析准确率也随之提高.     

计算机视觉中的Markov随机场方法

Markov随机场方法是计算机视觉中一个引人注目的新研究方向。该文论述了基于Markov随机场模型的分析框架和有关文献,评述了用于图像分割和复原的分析方法,探讨了它的发展动向。     

基于Markov过程的硬/软件综合系统可靠性分析

现代大型监控系统通常是一个复杂的硬/软件综合系统,其可靠性分析对于系统的设计、评估具有重要意义.综合考虑硬件、软件特点以及两者之间的相互作用关系,提出一种基于Markov过程的综合系统可靠性分析模型,模型中将系统失效分为硬件失效、软件失效与硬/软件结合失效.实际应用中,由于系统的状态数较大,提出利用循环网络方法对Markov状态转移方程进行求解,从而方便地得到系统处于各状态的瞬时概率与稳态概率.通过分析硬/软件综合系统可靠度、可用度与系统可靠性参数之间的关系,指出硬/软件结合失效将影响系统可用度,忽略硬/软件结合失效将导致可靠性估计值偏离实际值.     

两部件可修复温储备系统维修策略的改进

考虑由两个可修复元件、一个可修复的转换开关、一位固定的维修人员组成的两部件可修复温储备系统,为了提升系统可靠度,在维修策略上作了改进,提出了一位是固定维修工,两个及以上元件失效时雇佣一位临时工的方法。并建立了策略改进后所相应的Markov过程,通过Laplace变换给出了系统可靠性指标的计算模型,由案例分析获得策略改进后的模型可使系统的可靠性和可用度等指标在原有的基础上得以提高的结果,从而得到该策略的改进是合理和必要的结论。     

一种新的基于Markov链模型的用户行为异常检测方法

提出一种新的基于Markov链模型的用户行为异常检测方法。该方法利用一阶齐次Markov链对网络系统中合法用户的正常行为进行建模,将Markov链的状态同用户执行的shell命令序列联系在一起,并引入一个附加状态;在检测阶段,基于状态序列的出现概率对用户当前行为的异常程度进行分析,并根据Markov链状态的实际含义和用户行为的特点, 采用了较为特殊的判决准则。与Lane T提出的基于隐Markov模型的检测方法相比,该方法的计算复杂度较低,更适用于在线检测。而同基于实例学习的检测方法相比,该方法则在检测准确率方面具有较大优势。文中提出的方法已在实际入侵检测系统中得到应用,并表现出良好的检测性能。     

带丢包Markov切换线性系统的状态估计问题研究

针对一类带丢包的Markov切换系统,提出一种含有双Markov切换参数的交互式多模型算法.该算法利用一个二态的Markov链对系统是否丢包进行建模,得到双Markov链系统,通过定义乘积集将两个Markov切换参数所对应的模型集进行融合,并给出单个模型集中各模型与乘积集中各模型的对应关系.在此基础上,以交互式多模型算法为框架,采用分层的方法,并利用一种新的最优估计算法对双Markov链系统进行滤波.仿真实验证明了该算法的有效性.     

Petri网在系统可靠性分析中的应用

简单介绍了各种Petri网模型的定义和性质，对Petri网模型在系统可靠性分析中的基本行为描述、故障树表示与简化、故障诊断、可靠性指标的解析计算以及可靠性仿真分析等方面进行了较全面的评述，对进一步的研究方向进行了展望。     

用于移动路径预测的混合Markov 模型

分析了移动路径预测的已有方案,指出了各方案存在的问题。针对预测性能相对较好的K阶Markov预测器存在的状态空间膨胀问题,以2阶Markov预测器为例,提出了2步Markov模型,并验证了其在WLAN上的可行性。在此基础上,提出了一个混合多步Markov模型,并利用EM算法估计多个单步Markov模型的混合系数。该混合模型状态空间的复杂度为O(N),而2阶Markov模型为O(N2),其中,N为WLAN中的AP数目;混合模型的存储空间需求为O(N2),而2阶Markov模型为O(N3)。最后通过基于条件熵与误差向量2阶范数的计算分析以及基于实际数据集的实践分析表明,该混合模型能够以比2阶Markov模型小得多的空间代价获得与2阶Markov模型相似的预测精度和普适性,具有较高的实时在线应用价值。     

可维修闭环系统GO法等效代换思路

在交通物流,航空航天,国防工业等工程系统中,大多数是可维修的闭环系统。因此,研究闭环可维修系统可靠性分析中的GO法,并进行改进,很有价值。文中提出了闭环可维修系统可靠性基本模型和算法改进,并推导了计算过程,得到该基本模型下系统可靠性的计算公式,同时提出一种等效思想,将复杂系统等效成基本模型,只需找出两者之间的等效代换关系——等效失效率和等效修复率,将代换式带入基本计算公式中,直接得到复杂系统可靠性分析,该算法和思路为闭环系统的可靠性设计,维护,分析提供了理论依据和新的方法。     

Markov模型在功能安全温度变送器的应用

针对如何分析功能安全产品的可靠性问题,提出了一种基于Markov模型的可靠性分析方法。通过对产品的系统结构进行分析,研究系统的 Markov状态转移图,结合失效模式、影响及其诊断分析(FMEDA)法对系统进行可靠性建模,引入状态转移矩阵,并以功能安全温度变送器为例对模型进行验证。相关验证结果表明,该模型应用于功能安全领域是可行的。     

Effects of redundancy management on reliability modeling

Two methods are investigated for incorporating the effects of fault detection and isolation (FDI) decision errors and redundancy management (RM) policy into reliability models for a simple single-component-dual-redundant system. These two methods are combinatorial analysis and Markov chain modeling. Reliability analysts have traditionally chosen the classical combinatorial approach. However, the authors show that the existence of time-ordered event sequences resulting from the interaction of FDI decision errors with the RM policy considerably complicates the construction of the combinatorial model. An error analysis illustrates that a simplified combinatorial model, which ignores these time-ordered event sequences, inaccurately predicts the system reliability. The Markov modeling technique is an excellent alternative to the combinatorial approach because it easily and accurately accounts for time-ordered event sequences such as those present in fault-tolerant systems     

A Two-Stage Failure Model for Bayesian Change Point Analysis

This paper presents a new approach for detecting certain change-points, which may disturb the evaluation of reliability models with covariates, via a two-stage failure model, and stochastic time-lagged regression functions. The proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameterspsila posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.     

基于Object-Z与Markov链的校园卡系统测试用例

针对提高校园卡系统准确性和可靠性测试的要求,提出了Object-Z与Markov链结合的测试用例自动生成算法.使用Object-Z对系统进行形式化规约,生成测试场景和操作顺序图;将操作顺序图转换为Markov链使用模型;根据测试场景和Markov链使用模型生成数量相对合理的测试用例.该方法无需对系统进行运行,在需求分析与测试阶段就能对系统的功能进行测试.生成的校园卡系统测试用例证明该方法是有效的,并且在提高测试覆盖率的同时,使用Markov链也能保证对系统的可靠性测试.     

Approximate sensitivity analysis for acyclic Markov reliability models

Acyclic Markov chains are frequently used for reliability analysis of nonmaintained mission-critical computer-based systems. Since traditional sensitivity (or importance) analysis using Markov chains can be computationally expensive, an approximate approach is presented which is easy to compute and which performs quite well in test cases. This approach is presented in terms of a Markov chain which is used for solving a dynamic fault-tree, but the approach applies to any acyclic Markov reliability model.     

Importance and sensitivity analysis in assessing system reliability

After reviewing various importance concepts adopted in reliability, the authors propose a method for sensitivity analysis. The method uses the heuristically based generalized perturbation theory (GPT) methodology, widely adopted in reactor-physics studies. The concept of importance of a state in the Markov model representation of systems is introduced. The resulting formulations apply to any response of interest in reliability analysis. The relationship between the GPT method and Birnbaum importance is given     

A Markov chain model for an optical shared-memory packet switch

This paper first presents a Markov chain that exactly models an optical shared-memory packet switch. Without loss in model accuracy, this Markov chain state size is greatly reduced to form a reduced Markov chain (RMC). A simplified construction method is given to make the RMC tractable. Throughput and probability of packet loss derived using the RMC are also presented     

An improved neural network realization for reliability analysis

In this paper we present an improved neural network training algorithm and architecture for reliability analysis of a simplex system and a TMR system which includes the effects of permanent fault and intermittent fault. A fully-connected three-layer neural network represents a discrete-time n-state reliability Markov model of a fault-tolerant system. The desired reliability of the system is fed into the neural network, and when the neural network converges, the design parameters are retrieved from the weights of the neural network. Finally, the simulation results show that the proposed method converges faster than other methods, especially in the case of the state number of the Markov model, which increases. This technique is also suitable for any system.     

Determination of Reliability Parameters of a Digital Multiplex Frame Strategy Using Markov Chains and a State Reduction Method

This paper discusses the analysis of a digital multiplex framing strategy to determine certain reliability parameters for the frame synchronization system. The only model found suitable for determining the desired parameters is a Markov chain with ~ 5 × 105 states. To make meaningful calculations using this Markov chain, a state reduction method is employed. This method makes possible a determination of mean time between failures, availability, and several other parameters as a function of channel error rate. The model and analysis are applied to a framing strategy of greater complexity than those reported in the literature. The analysis makes it possible to include several effects, such as channel errors during the frame search operation, which are not possible with other methods.     

一种改进的用于可靠性分析的神经网络的实现

针对容错系统的可靠性问题,建立基于马尔科夫模型的三层前馈神经网络。提出一种改进的神经网络训练算法,用于包含永久过错．瞬态过错和周期过错影响的一个三模冗余（TMR,Triple Modular Redundancy）系统的可靠性分析。一个全连接的三层神经网络表示一个容错系统的离散时间n状态马尔科夫模型的可靠性。将系统的期望可靠性反馈入网络,当神经网络收敛时,从神经网络的权值中得出设计参数。仿真结果显示,与四层神经网络相比．三层神经网络收敛得更快．收敛可靠十牛更椿沂期望可靠性。