可列m重非齐次马氏链的遍历性与熵率

本文在研究可列m重非齐次马氏链各种遍历性定义的基础上,以转移概率引入可列m重非齐次马氏链绝对平均强遍历性的概念,通过设定可列m重非齐次马氏链满足这种强遍历的充分条件,得出可列m重非齐次马氏链泛函的一个极限定理,并应用此极限定理得到了可列m重非齐次马氏链熵率存在的一个定理。     

二叉树上非齐次分支马氏链一类强极限定理

近年来树图或者树形网络等诸多复杂系统的结构性质与极限性质逐渐成为研究的热点问题,特别是在树指标马尔可夫链领域的研究中,国内外学者们取得了丰富的研究成果.二叉树上非齐次分支马尔可夫链作为一类特殊的树指标马尔可夫链,该模型的极限性质被国内外学者的广泛研讨并应用于生物动力学、信息论等诸多领域.本文致力于研究在有限状态空间空间取值的二叉树上非齐次分支马尔可夫链转移概率调和平均的极限性质以及该性质与树指标马尔可夫链模型之间的联系.首先在新的条件下,本文给出了在有限状态空间中取值的二叉树上非齐次分支马氏链的强极限定理,并进一步得到了其随机转移概率调和平均的强极限定理,最后借助于两类模型之间的等价关系以及平均值不等式,推广了树指标非齐次马氏链随机转移概率的极限定理。     

无规则性概念的推广与可列非齐次马氏链的一类极限定理

给出了对可列非齐次马氏链普遍成立的一类极限定理，其中包含我规则性概念的推广。     

齐次树指标随机场的一类Shannon—McMillan偏差定理

本文通过齐次树指标任意随机场与马氏链场相比较,研究齐次树上任意随机场用不等式给出的一类Shannon-McMillan极限定理,即随机偏差定理,为进一步研究树图随机场的信息编码提供了理论基础.     

非齐次马氏链样本相对熵率存在定理

样本相对熵率是信息论中一个重要的内容,在统计假设检验及编码理论中起着非常重要的作用.本文的目的是要研究在有限状态空间中取值的非齐次马氏链样本相对熵率的存在性.首先将数列绝对平均收敛的定义推广到平面上,并得到平面点列绝对平均收敛的定义及相关引理,然后利用非齐次马氏链二元函数的一类平均极限定理及强大数定律,给出非齐次马氏链样本相对熵率存在的条件.本文将信息论中关于独立同分布随机变量序列的假设检验问题做了更为广泛的推广.     

关于齐次树指标马氏链的广义熵遍历定理

本文主要研究有限状态齐次树指标Markov链的强大数定律和广义熵遍历定理．熵遍历定理研究的是信息论中信源的渐近均分割性,树指标Markov链是近年来概率论的研究方向之一．首先,参照非齐次Markov链广义熵密度概念,本文给出了树指标Markov链的广义熵密度的定义．然后,通过构造一组期望值为1的随机变量,利用Markov不等式和Borel-Cantelli引理,证明得到了定义在树指标Markov链上一类随机变量的延迟平均的强极限定理．最后,利用上述定理的推论,我们证明得到了Cayley树上有限状态Markov链状态出现次数的延迟平均的强大数定律和广义熵遍历定理．本文的结果是对一些已有结果的推广．     

关于非齐次马多链的Cesaro平均收敛性

首先证明了齐次马氏链一个Ｃｅｓａｒｏ平均收敛定理，它是Ｂｏｗｅｒｍａｎ等人的一个结果的推广，本文利用这个收敛定理给出非齐次马氏链一元泛函的一个极限定理，并讨论了这个极限定理在马氏决策过程和信息论中的应用。     

关于非齐次马氏链的Cesaro平均收敛性

首先证明非齐次马氏链一个Ｃｅｓａｒｏ平均收敛定理，它是Ｂｏｗｅｒｍａｎ等人一个结果的推广，本文利用这个收敛定理给出非齐次马氏链一元泛函的一个极限定理，并讨论这个极限定理在马氏决策过程和信息论中的应用     

关于非齐次马氏链信源的一个编码定理

本文旨在将无记忆离散信源的编码定理推广至非齐次马尔科夫链情形,以扩展无记忆离散信源编码定理的适用范围．利用经典的波莱尔-坎特利引理,建立关于非齐次马尔科夫链延迟平均的强大数定理,应用独立随机信源逼近非齐次马氏信源,从而获得非齐次马氏信源的广义编码定理．最后运用得到的广义编码定理,给出分批数据假设检验问题中可容忍错误概率的最小值的计算方法．     

关于树指标隐Markov链及其等价定义

本文参照直线上隐Markov模型的概念,给出有限树指标隐Markov链的定义.在该定义中,树指标隐Markov链由两个树指标随机过程组成,其中第一个树指标随机过程是树指标Markov链,是不能被直接观测到的隐藏链;第二个树指标随机过程是可被观测的且关于第一个树指标随机过程条件独立,对于树上的任意一个顶点,第二个随机过程此处的取值只信赖于隐藏链中此处的取值.最后,我们给出了树指标隐Markov链的三个等价定义.     

Markovian models for deadlock analysis in automated manufacturing systems

Deadlocks constitute a major issue in the desing and operation of discrete event systems. In automated manufacturing systems, deadlocks assume even greater importance in view of the automated operation. In this paper, we show that Markov chains with absorbing states provide a natural model of manufacturing systems with deadlocks. With illustrative examples, we show that performance indices such as mean time to deadlock and mean number of finished parts before deadlock can be efficiently computed in the modelling framework of Markov chains with absorbing states. We also show that the distribution of time to deadlock can be computed by conducting a transient analysis of the Markov chain model.     

Gastric esophageal surgery risk analysis with a fault tree and Markov integrated model

Reliability methods have been widely used in risk analysis of medical surgeries. In this study, the authors combine a fault tree with Markov models to assess time independent- and dependent factors together. Dynamics are integrated in the traditional fault tree, and meanwhile the processes of solving Markov are simplified with the modular approach. Continuous time Markov chains are adopted in evaluating the failure probability of a gastric esophageal surgery after categorizing basic events in the fault tree, and a certain time dependent variables, such as failure rate of medical equipment, surgery frequency, and rescue timeliness are involved into risk analysis. A case is studied with data collected from a general hospital, to illustrate the operational process of the proposed method. Results based on the inputs show that taking rescue actions into consideration can reduce the gap between the result of fault tree analysis and the reality. Sensitivity analysis for measuring the impacts of the above time relevant variables is conducted, as well as limitations of the Markov model are discussed.     

An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis

Approximate Markov chain method for dynamic fault tree analysis is suggested for both reparable and non‐reparable systems. The approximation is based on truncation, aggregation and elimination of Markov chain states during the process of dynamic fault tree transformation to corresponding Markov chain. The method is valid for small probabilities. For reparable systems, it is true if mean time to repair is much less than mean time to failure. Several examples are studied. Additional simplification is considered in case the system is in a steady state. Copyright © 2015 John Wiley & Sons, Ltd.     

Berechnung der Verfügbarkeit der Kraftwerke mit Hilfe der Markoff-Kette

In the paper power plant operation is modelled using continuous time Markov chains with discrete state space. The model is used to compute the power plant reliability and the importance and influence of individual states, as well as the transition probabilities between states. For comparison the model is fitted to data for coal and nuclear power plants recorded over several years.     

Quantitative analysis of a fault tree with priority AND gates

A method for calculating the exact top event probability of a fault tree with priority AND gates and repeated basic events is proposed when the minimal cut sets are given. A priority AND gate is an AND gate where the input events must occur in a prescribed order for the occurrence of the output event. It is known that the top event probability of such a dynamic fault tree is obtained by converting the tree into an equivalent Markov model. However, this method is not realistic for a complex system model because the number of states which should be considered in the Markov analysis increases explosively as the number of basic events increases. To overcome the shortcomings of the Markov model, we propose an alternative method to obtain the top event probability in this paper. We assume that the basic events occur independently, exponentially distributed, and the component whose failure corresponds to the occurrence of the basic event is non-repairable. First, we obtain the probability of occurrence of the output event of a single priority AND gate by Markov analysis. Then, the top event probability is given by a cut set approach and the inclusion–exclusion formula. An efficient procedure to obtain the probabilities corresponding to logical products in the inclusion–exclusion formula is proposed. The logical product which is composed of two or more priority AND gates having at least one common basic event as their inputs is transformed into the sum of disjoint events which are equivalent to a priority AND gate in the procedure. Numerical examples show that our method works well for complex systems.     

Markov models of integrating spheres for hyperspectral imaging

Theoretical models of the signal detected by a CCD camera during hyperspectral imaging with an integrating sphere are derived using Markov chains with absorbing states. The models provide analytical expressions that describe the real reflectance of the sample as a function of the detected signal at each pixel of the image. Validation of the models was done by using reflectance standards and tissue phantoms. The models provide accurate analytical solutions for samples and spheres that are near-Lambertian reflectors.     

Loading state in spot‐like overlap joints determined by correlation of deformation patterns

The loading state in spot‐like overlap joints (spot‐welded, clinched, riveted, bolted or others) can be determined by evaluating measured surface strains around the local joint (e.g. a weld spot). The measured strain pattern is decomposed into proportions of reference patterns representing simple basic loading states. Correlation analysis on the basis of Markov chains is used to achieve this.     

Aggregated Markov-based reliability analysis of multi-state systems under combined dynamic environments

Reliability of a system may differ greatly when operating under different environments. However, the existing works have either neglected the environment factor in system reliability analysis or considered this factor for binary systems or systems subject to a single environment (parameter). In this paper, we make contributions by modeling a multi-state system operating under hybrid dynamic environments affected by multiple environmental parameters. Different Markov chains with finite states are used to represent the random system behavior and dynamic environments, leading to an aggregated Markov process that models the overall system behavior. An effective approach based on state partitions and aggregations is suggested for assessing the system reliability indexes, including reliability, availability, multi-point availability, and environment-based reliability. A high-pressure homogenizer system is analyzed to demonstrate the proposed model and show the comparison of the reliability of system under fixed and dynamic environment.