Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses.Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems.Fault-tolerant softwares are used to increase the overall reliability of software systems.Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme),fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme).These softwares incorporate the ability of system survival even on a failure.Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems.Most of them consider the stable system reliability.Few attempts have been made in reliability modeling to study the reliability growth for an NVP system.Recently,a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency.In this model,a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed.In this paper,we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation.Using this model,a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system.The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required.It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost.In this paper,we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.     

Approximate dynamic programming solutions with a single network adaptive critic for a class of nonlinear systems

Approximate dynamic programming (ADP) formulation implemented with an adaptive critic (AC)-based neural network (NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman (HJB) equations. As interest in ADP and the AC solutions are escalating with time, there is a dire need to consider possible enabling factors for their implementations. A typical AC structure consists of two interacting NNs, which is computationally expensive. In this paper, a new architecture, called the ‘cost-function-based single network adaptive critic (J-SNAC)’ is presented, which eliminates one of the networks in a typical AC structure. This approach is applicable to a wide class of nonlinear systems in engineering. In order to demonstrate the benefits and the control synthesis with the J-SNAC, two problems have been solved with the AC and the J-SNAC approaches. Results are presented, which show savings of about 50% of the computational costs by J-SNAC while having the same accuracy levels of the dual network structure in solving for optimal control. Furthermore, convergence of the J-SNAC iterations, which reduces to a least-squares problem, is discussed; for linear systems, the iterative process is shown to reduce to solving the familiar algebraic Ricatti equation.     

Software Operational Profile Based Test Case Allocation Using Fuzzy Logic

Software operational profile (SOP) is used in software reliability prediction,software quality assessment,performance analysis of software,test case allocation,determination of"when to stop testing,"etc.Due to the limited data resources and large efforts required to collect and convert the gathered data into point estimates,reluctance is observed by the software professionals to develop the SOP.A framework is proposed to develop SOP using fuzzy logic,which requires usage data in the form of linguistics.The resulting profile is named fuzzy software operational profile (FSOP).Based on this work,this paper proposes a generalized approach for the allocation of test cases,in which occurrence probability of operations obtained from FSOP are combined with the criticality of the operations using fuzzy inference system (FIS).Traditional methods for the allocation of test cases do not consider the application in which software operates.This is intuitively incorrect.To solve this problem,allocation of test cases with respect to software application using the FIS model is also proposed in this paper.     

Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between healthcare service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the effciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents.     

An Efficient Computational Method for Solving Nonlinear Matrix Equation and Its Application in Queuing Analysis

The matrix analytic analysis of queues with complex arrival,vacation and service characteristics requires the solution of nonlinear matrix equation.The complexity and large dimensionality of the model require an effcient and smart algorithm for the solution.In this paper,we propose and efficient Adaptive Newton-Kantorovich(ANK) method for speeding up the algorithm solving the nonlinear matrix equation which is an inevitable step in the analysis of the queue with embedded Markov chain such as BMAP/SMSP/1/∞ queue or its discrete version.BMAP/SMSP/1/∞ is a queuing model with a Semi Markov Service time Process (SMSP) and a Batch Markovian Arfival Process(BMAP).The numerical result is presented for the discrete case of N-MMBP/D/1 queue which arises in analyzing traffic aspect of computer communication network,where MMBP is Markov Modulated Bermoulli Process.The comparisons of Adaptive Newton-Kantorovich(ANK)with Modified Newton-Kantorovich(MNK) show that ANK saves 30% of CPU tim when the number of user N is 50.     

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.     

Fuzzy reliability of an iSCSI-based disk array system

iSCSI is a newly emerging protocol with the goal of implementing the storage area network （SAN） technology over TCP/IP, which brings economy and convenience whereas it also raises performance and reliability issues. This paper presents a implementation of iSCSI-based disk array system, and then analyzed the reliability of this system by combination of fuzzy logic and Markov modeling. This reliability method is a technique for analyzing fault tolerant designs under considerable uncertainty, such as is seen in compilations of component failure rates, the presented model provides the estimation of the lower and upper boundary of iSCSI-based disk array system with a single run of the model.     

Distributed adaptive Kalman filter based on variational Bayesian technique

In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The convergence of the proposed algorithm is proved in two main steps: noise statistics is estimated, where each agent only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.     

On environment-driven software model for Internetware

Internetware is envisioned as a general software paradigm for the application style of resources integration and sharing in the open, dynamic and uncertain platforms such as the Internet. Continuing the agent-based Internetware model presented in a previous paper, in this paper, after an analysis of the behavioral patterns and the technical challenges of environment-driven applications, a software-structuring model is proposed for environment-driven Internetware applications. A series of explorations on the enabling techniques for the model, especially the modeling, management and utilization of context information are presented. Several proto-typical systems have also been built to prove the concepts and evaluate the techniques. These research efforts make a further step toward the Internetware paradigm by providing an initial framework for the construction of context-aware and self-adaptive software application systems in the open network environment.     

基于多重马尔可夫Bayes网的软件失效预测模型

软件开发的复杂性决定了理想的软件可靠性模型既应具有包容众多复杂因素的能力,又要有构造灵活的功能。迄今为止,人们提出的众多模型,由于设定了很多近乎苛刻的条件,使它们难以具有普适性。Bayes网提供了解决这一问题的有力工具,论文就利用多重马尔可夫Bayes网建立起基于Bayes网的软件失效预测模型,并对此进行了详细的分析,给出了模型的求解步骤。最后,通过实例验证了该模型的有效性。     

基于Bayes网的软件残留错误数度量

软件开发的复杂性决定了理想的软件复杂性度量模型既应具有包容众多复杂因素的能力,又要有构造灵活的功能。迄今为止，人们提出的众多模型，由于设定了很多近乎苛刻的条件，使它们难以具有普适性。Bayes网提供了解决这一问题的有力工具。该文建立起一种基于Baycs网的软件残留错误数度量模型，并对此进行了分析。     

基于贝叶斯网络的风力发电机故障诊断方法

贝叶斯网络是概率统计学的重要分支,具有强大的不确定性问题处理能力,适用于复杂系统的故障诊断。风力发电机系统维护成本较高,为减少维修成本,需要进行准确的故障定位;文章对基于贝叶斯网络的故障诊断方法进行了研究,介绍了贝叶斯网络故障诊断模型的建立过程,并着重介绍了诊断算法推导和计算过程;利用历史故障统计数据建立了风力发电机系统贝叶斯网络Matlab模型,主要包括网络结构有向无环图和条件概率分布参数等内容;最后,模拟了两种故障,分别采用贝叶斯网络方法和相关性矩阵方法进行故障诊断,通过对两种方法诊断结果的比较,前者具有更好的故障分辨率,可有力支持复杂系统的维护保障、降低维修成本。     

面向广义质量体系的软件质量贝叶斯评估模型

现有软件质量评估模型主要关注软件系统的基本质量特性,缺乏考虑顾客价值特性和开发商组织管理特性,不能全面和科学地评估软件系统质量.文章以贝叶斯网络刻画了广义质量特性变量间复杂依赖关系,构建了更有针对性的软件质量量化评估模型.应用实例表明,该模型能综合考虑软件系统广义质量特性,对软件质量作出合理评价,并能依据贝叶斯网络的反向推理功能找到影响软件质量的关键因素.     

可能网络结构学习的依赖分析方法研究

贝叶斯网络是概率理论与图形模式的结合,被广泛用于不确定性问题求解,但不具有处理不准确性信息的能力。可能网络是可能理论、概率理论与图形模式的结合,可弥补贝叶斯网络这方面的不足。首先介绍关于可能网络的一些概念,并与贝叶斯网进行比较,然后给出一种基于依赖分析的可能网络结构学习方法。     

贝叶斯网络法的电源变电站供电可靠性分析

变电站的可靠性直接影响到电力系统的性能。为此,提出了基于贝叶斯网络法的电源变电站供电可靠性分析方法。首先深入探究供电可靠性的影响因素,定义电源变电站构成单元,构建电源变电站故障树模型,然后分解共因失效组,结合电源变电站整体架构,构建贝叶斯网络模型,应用贝叶斯网络工具箱,计算电源变电站供电可靠性指标值,最后对具体电源变电站供电可靠性进行定量分析。结果表明：在不同工况背景下,本文方法的供电可靠性评估误差小,满足电源变电站的正常供电需求,具有一定的实际应用价值。     

贝叶斯网络结构模型的构建

贝叶斯网络结构是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的表示模型,它描述了数据项及其依赖关系,并根据各个变量之间概率关系建立图论模型,但是如何获取具有丢失数据的网络结构是一个急需解决的问题.本文提出一个基于Kullback-Leibler(KL)散度的贝叶斯网络结构学习的KLBN(Kullback-Leibler Bayesian Network)算法.实验结果表明,KLBN算法在可靠性方面明显优于传统的具有丢失数据的贝叶斯网络结构学习算法.     

一种基于贝叶斯网络的作战重心评估模型

作战重心（Center of Gravity）是指战役体系中敌我双方的关键环节。作战重心评估是一个经验性、模糊性的过程。贝叶斯网络作为一种不确定知识表示模型,具有概率论及图论基础,对于解决复杂系统决策问题具有较强的优势,适合用于作战重心评估。文中提出并实现了一种基于贝叶斯网络推理的作战重心评估模型。通过该模型,可以定量地评估各个环节对于证据的重要程度,从而确定该作战过程中的作战重心。文中使用联合树（Clique Tree）算法进行贝叶斯网络精确推理,并详细阐述了推理过程中联合树建立,消息传递的过程。最后通过实例验证,基于贝叶斯网络推理的模型能够有效地对作战重心进行定量的评估。     

基于贝叶斯网的分布式软件行为运行时可信性分析

在开放、动态和复杂的网络环境中,监测与分析软件行为可信对现代分布式软件是至关重要的.针对分布式软件运行时的外在表现特征,系统地收集相关数据,根据具体交互场景建立贝叶斯网模型.利用此模型,在上下文环境中通过监测相关的数据来对软件行为运行时可信性进行分析.建网过程中,文中提出了使用“3σ原则”来离散化连续型随机变量,其在判断样本标注异常及先验参数确定等方面具有独特优势,操作方便又符合实际情况,且提高了参数学习效率;同时,文中提出了分层方法构造先验贝叶斯网思想,通过计算节点间的相关系数来逐步修正贝叶斯网结构,降低了建网的复杂性和误差.通过仿真实验,证实了本文所提出的方法在软件行为可信性分析方面较其他方法有着独特的优势.     

基于贝叶斯网络的不确定性知识处理研究

贝叶斯网络因其在处理不确定性知识方面的优势近来受到数据挖掘等领域的重视。与当前流行的数据挖掘算法包括决策树、神经网络和遗传算法等相比，贝叶斯网络更易于理解，且有很好的预测效果，适用于处理那些本身存在着固有的不确定性的领域。在比较了贝叶斯网络处理不确定性知识的优势的基础上，描述了用贝叶斯网络进行数据挖掘的过程及其主要研究方向，最后对贝叶斯网络的应用领域、研究现状和前景进行了分析和展望。