基于扩展马尔科夫链的攻击图模型

在网络风险评估中,攻击图模型用于表达网络存在的脆弱点以及脆弱点之间的关联关系,传统方法通过主观经验确定攻击图中状态转移的权重,给状态转移概率的确定带来较大不准确性。为提高其准确性,通过将脆弱点的攻击难度作为状态转移概率的确定标准,构建基于马尔科夫链的攻击图模型。搭建实验环境,将该模型应用到实际环境中,得到了准确的状态转移概率。该模型摒弃了传统概率确定方法中的主观性,有效提高了概率确定的准确性。     

基于马尔科夫链的平台生存任务模型与航路评估

在地面雷达武器威胁环境里,不仅需要评估航路上飞行平台的有关生存和任务完成的概率,而且还要能度量不同航路的好坏。由于传统模型不能有效表达生存模型和任务模型,更不能有效统一生存模型与任务模型,为此建立了基于马尔科夫链的生存任务模型,并且在此模型的基础上引入代价来评估航路。最后的仿真结果表明,该方法有效刻画了生存率和任务完成率,能关注到完成任务且无伤害返回的概率,并且能有效度量航路的好坏。     

基于马尔科夫链的主机异常检测方法研究

提出了基于马尔科夫链模型的主机异常检测方法,首先提取特权进程的行为特征,并在此基础上构造Markov模型。由Markov模型产生的状态序列计算状态概率,根据状态序列概率来评价进程行为的异常情况。利用Markov模型的构造充分提取特权进程的局部行为特征的相互关系。实验表明该模型算法简单、实时性强、检测率高、误报率低、适合用于在线检测。     

基于马尔科夫链的网络控制系统调度

网络控制系统数据包的丢失将导致控制系统性能下降与网络资源利用率降低。以马尔科夫链对网络控制系统的数据包丢失时延进行动态预测,并将它作为网络利用率的一个重要参数,利用反馈原理动态地调整网络控制系统控制任务周期,在线分配网络资源,实现调度与控制的集成,提高了网络资源的利用率与控制系统性能,仿真结果表明了该方法的有效性。     

基于马尔科夫链的民族地区毒品犯罪预测研究

毒品的非法生产、需求以及贩运呈现出一种巨大规模的上升趋势,特别是边疆少数民族地区的毒品犯罪问题更加引人关注。针对此问题,大多是从社会学的角度进行研究,提出了采用马尔科夫链的方法,对边疆少数民族地区收集的犯罪数据进行统计分析、建模。实验结果表明,预测毒品犯罪案件数与实际发生的毒品犯罪案件数相比,达到了96．52％的预测准确率。结论是,通过马尔科夫链来进行民族地区的毒品犯罪预测研究,可获得更加合理、准确的预测结果,具有更高的有效性和实用性。     

一种基于马尔科夫链的冲突证据组合方法

针对智能信息处理中Dempster组合规则不能处理高度冲突的问题,考虑到序贯证据的序列性具有高效的抗干扰性能,因此本文提出了一种基于马尔科夫链的冲突证据组合方法. 首先,从经典马尔科夫链中的确定性状态描述扩展到不确定性状态描述;然后,以滑动窗口宽度l对序贯历史证据进行采样, 并利用相似性测度计算的权重来修正它们,从而对修正后的历史证据进行马尔科夫建模,并根据转移概率矩阵,计算证据代表;最后,利用Murphy组合规则对该证据代表组合l-1次. 当然,本文方法也比较适合批量同步融合. 大量的仿真实验对比分析表明,该方法优势比较明显, 有效地解决了冲突证据合成出现的问题,并能有效兼顾合成结果的鲁棒性和灵敏性.     

基于马尔科夫链的组密钥更新代价分析方法

组密钥管理机制大多基于树结构或星结构。在这两种结构中,成员的加入和离开均对密钥更新代价影响很大。提出基于连续时间马可夫链的代价更新分析方法,利用连续时间马可夫链来模拟成员动态变化,并对星结构和树结构的组播密钥管理方案进行更新代价仿真分析,并比较了两者的性能差异。仿真结果表明,组成员离开的概率和速率是影响星结构和树结构密钥管理方案在密钥更新代价上差异的主要因素。因此,在动态变化的环境下,应使用星结构和树结构相结合的组密钥管理机制。     

基于马尔科夫链的随机测量矩阵研究

测量矩阵是压缩感知理论中的重要组成部分,其将直接影响原始信号的重构精度.针对常用测量矩阵重构精度较低的问题,构造一种基于马尔科夫链的随机测量矩阵.利用马尔科夫链的随机性生成M个随机数,将随机数按照规则分别映射为-1和1后作为M×M维对角矩阵的元素,采用马尔科夫链生成M×(N-M)个随机数并按照规则分别映射为0和1,构成包含0和1元素的M×(N-M)维矩阵,并将这两部分矩阵相结合形成M×N的测量矩阵.仿真结果表明,该矩阵结构简单,相比常用测量矩阵和基于奇异值分解的Toeplitz结构矩阵重构精度得到明显提升,并且减少了运算量与存储空间.     

马尔科夫链在预测中的应用

提出利用马尔科夫链预测股票第二天的最大涨幅.建立了算法模型,采用具体实例,介绍了算法的过程,并利用C#实现了该算法.     

基于图像处理的高精度仪表判读方法

提出了用图像技术对高精度指针式仪表自动判读的方法,关键问题是视差的检测和仪表图像坐标系统与仪表读数跟踪系统之间坐标的转换。采用脊波提取仪表图像中的直线,利用图像信息控制数控系统动作。实验表明:该读数方法绝对误差为零,非常适合于高精度指针式仪表自动跟踪判读。     

Non-stationary fuzzy Markov chain

This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationary fuzzy Markov chain model is proposed in an unsupervised way, based on a recent Markov triplet approach. The method is compared with the stationary fuzzy Markovian chain model. Both stationary and non-stationary methods are enriched with a parameterized joint density, which governs the attractiveness of the neighbored states. Segmentation task is processed with Bayesian tools, such as the well known MPM (Mode of Posterior Marginals) criterion. To validate both models, we perform and compare the segmentation on synthetic images and raw optical patterns which present diffuse structures.     

Robust Partially Mode Delay‐Dependent ℋ∞ Output Feedback Control of Discrete‐Time Networked Control Systems

In this paper, a methodology for designing an output feedback controller for discrete‐time networked control systems has been considered. More precisely, network‐induced delays between the sensor and the controller is modelled by a Markov chain with transition probabilities which are not assumed to be fully known. The systems parameter uncertainties are assumed to be norm‐bounded and possibly time‐varying. To the best of the authors knowledge, the problem of designing a partially mode delay‐dependent output feedback controller for NCSs with partially known transition probability matrix has not been investigated in the literature. Based on the Lyapunov‐Krasovskii functional approach, sufficient conditions for the existence of a robust partially mode delay‐dependent output feedback controller are given in terms of bilinear matrix inequalities which can be solved using a cone complementarity linearization algorithm. The proposed design methodology differs from the existing design methodologies in that dynamic output feedback controllers are parameterized by both modes and transition probabilities, as opposed to the existing design approaches which parameterize controllers by modes only. The results obtained reduce to the existing results on fully known transition matrices when transition probabilities are fully known. It is shown that the proposed methodology can be applied to real world systems. The proposed design methodology is verified by using a DC servo motor system where the plant and the controller are connected via a cellular network with partially known transition probability matrix.     

相关观测噪声下的动态定位分析

对车载环境下的定位误差作时间序列分析．得到二阶马尔可夫过程的定位误差模型。将定位误差作为观测噪声．以此建立的观测方程中的观测噪声具有时间相关性。利用观测量相差法,把相邻两次观测量的相关部分消去,建立起观测噪声为白噪声的观测方程,并推导出状态估计的递推方程。避免了高阶矩阵的求逆,有效地减少了计算量,并降低了对内存的需求,提高了车载动态定位系统的实时性和定位精度。仿真结果进一步证明了算法的有效性。     

Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data

In this work, we investigate the application of modeling alternatives regarding fuzzy Markov chain-based, multitemporal, cascade classification of remote sensing data. From a theoretical viewpoint, alternative designs for the fuzzy Markov chain-based model are formally presented. From a pragmatic perspective, experimental results are discussed and analyzed, providing a deeper understanding of the virtues and odds of multitemporal remote sensing data classification based on fuzzy Markov chains. We claim that the key components of the fuzzy Markov chain-based, multitemporal classification model with respect to its alternative designs are the t-norm and s-norm operators, and the fuzzy aggregation function. The main objective of this paper is to investigate how a particular design may affect the classification performance. In addition, this paper aims at assessing the impact of the monotemporal classifiers’ accuracies on the quality of the multitemporal classification outcome, according to the selected design alternatives. In conclusion, this paper presents design guidelines for both the developer of image analysis systems and the designer of classification methods based on fuzzy Markov chains.     

Perfect simulation for marked point processes

Some recently proposed exact simulation methods are extended to the case of marked point processes. Four families of algorithms are considered: coupling from the past, the clan of ancestors technique, the Gibbs sampler, and a Metropolis-Hastings algorithm based on birth and death proposals. From a theoretical point of view, conditions are given under which the algorithms yield unbiased samples in finite time. For practical application, a C++ library for marked point processes is described. The various algorithms are tested on several models, including the Widom-Rowlinson mixture model, multi-type pairwise interaction processes, and the Candy line segment model. A simulation study is carried out in order to analyse the proposed methods in terms of speed of convergence in relation to the parameters of the model. For the range of models investigated, the clan of ancestors algorithm using the incompatibility index is the fastest method among the ones analysed, while coupling from the past is applicable to the widest range of parameter values, and usually faster than the Metropolis-Hastings sampler. The latter two methods tend to be cumbersome if the underlying model is neither attractive nor repulsive. If one is prepared to approximate by discretisation, a proper choice of Gibbs sampler makes it possible to obtain samples from models that lack monotonicity or have such a high local stability bound as to rule out coupling from the past or clan of ancestor approaches in practice.     

流程片段自适应重用策略研究

近年来,自适应软件是软件工程领域的研究热点.研究者们从不同角度对如何促进和提高软件系统的自适应进行了大量研究,有的以体系结构为中心研究软件的自适应,有的则从需求的角度进行研究.但是,从软件系统的流程片段自适应重用的角度来研究软件自适应,类似的研究工作还很少.借鉴软件控制论中的思想来研究流程片段的自适应重用,基于受控的Markov链模型来探讨流程片段的最优查询策略.提出了针对流程片段查询特殊应用环境下的CMC(controlled Markov chain)模型,并对该模型进行了优化处理.基于逐次最小二乘法,进一步提出了流程自适应查询策略,该策略充分利用流程片段的历史查询信息,通过在线参数调整,能够帮助查询人员及时调整和优化查询策略.Matlab环境下的仿真实验和真实流程数据集下的实验,共同验证了该模型和算法的有效性和可行性.     

Impact of demand correlation and information sharing in a capacity constrained supply chain with multiple-retailers

This paper considers a single product, two-echelon capacity constrained supply chain consisting of a supplier and two retailers facing correlated end-item demand. We use a decentralized Markov decision process with restricted observations to represent this system and conduct a numerical study to quantify the benefits of information sharing to the retailers under varying levels of supplier capacity and supply allocation mechanisms. Our results show an inverse relationship between capacity and information and indicate the retailers can achieve significant benefits as a result of the information sharing partnership.     

基于图像处理技术的机械零件小孔径尺寸测量方法

提出一种基于图像处理技术的机械零件小孔径尺寸测量方法,解决传统上用止通规人工进行检测的各种不便;图像处理技术主要包括图像的平滑、灰度拉伸、二值化、图像象素的计算等处理;初步探讨了利用图像处理技术进行机械零件小孔径的尺寸测量的方法,并通过实例,从理论上和实践上证明本方法的可行性和正确性.     

The use of learning algorithms in telephone traffic routing—A methodology

A theoretical as well as conceptual framework for the use of learning algorithms in telephone traffic routing is given. The approach is distinctly different from the mathematical programming methods generally used in such cases. Learning algorithms at the network nodes update their strategies for routing traffic on the basis of success or failure in completing calls. The entire system is described as a Markov process and different learning schemes are shown to lead to different flow patterns in the steady state.