基于贝叶斯网络的组合服务信任度评估方法

对组合服务信任度评估问题进行了研究。基于信任的不确定性和模糊性,给出了信任度的离散化表示方法;借助于贝叶斯网络的强大的推理能力,给出了组合服务的贝叶斯网络模型以及组合服务模型到贝叶斯网络的转换规则,利用贝叶斯网络的正向推理机制设计了基于贝叶斯网络模型的组合服务信任度评估方法。通过一个组合服务的买例,说明了组合服务的贝叶斯网络模型及其基于贝叶斯网络的组合服务信任度评估方法的使用方法。该评估方法基于信任的模糊性和不确定性,更符合信任的本质并易于实现。     

基于贝叶斯网络的自由场地震液化沉降评估

基于贝叶斯网络方法,综合考虑地震参数、土体参数和场地条件等12个影响因素,结合场地的液化势和液化潜能指数,建立了地震液化沉降的贝叶斯网络评估模型。通过算例分析,与径向基神经网络方法和IY简化计算方法的评估性能对比,发现地震液化沉降的贝叶斯网络评估模型的优势明显;该模型不仅有较好的评估精度和可靠性,而且还可以进行逆向因果推理。对两个机器学习模型进行敏感因素分析发现,在12个影响因素中,地表峰值加速度、地震持续时间和标准贯入锤击数为较敏感因素,和IY简化算法考虑的参数基本一致。     

基于贝叶斯网络的VDT作业疲劳

在分析VDT作业中引起疲劳的各种影响因素时,提出利用贝叶斯网络分析疲劳的方法,建立疲劳先验贝叶斯网络,通过实证研究建立后验贝叶斯网络,利用Netica软件对后验贝叶斯网络模型进行数据分析解释及推理,以期找到影响疲劳的关键因素。     

基于贝叶斯网络的计算机网络端到端服务故障诊断

研究了利用贝叶斯网络不确定推理技术实现端到端服务故障诊断的方法，详细描述了贝叶斯网络故障诊断模型的建立方法，设计了基于Pearl信念传播机制的故障诊断算法，并对其进行了改进，以提高诊断效果．最后，通过仿真验证了该方法的有效性，并提出了下一步的研究方向．     

基于贝叶斯网络的计算机网络设备级故障诊断

贝叶斯网络能够用图形化的方式表示对象间的依赖关系,并支持不确定推理。研究基于贝叶斯网络的计算机网络设备级故障诊断方法,描述故障诊断模型的构造方法,设计故障诊断算法,并通过仿真验证该方法的有效性。     

基于代数决策图的贝叶斯网络参数简化技术

贝叶斯网络是一种进行不确定性知识表达和推理的有效工具,推理算法是贝叶斯网络研究的主要内容之一.目前,贝叶斯网络推理算法采用条件概率表(CPT)来存储贝叶斯网络中各节点的条件概率分布(CPD).CPT中的概率参数随父节点数目的增加呈指数增长,使得网络中概率参数急剧增加,降低了网络推理效率.为提高网络推理效率,本文提出采用代数逻辑图(ADD)取代CPT存储网络中各节点CPD的方法.结合有序二分决策图理论,分析并验证了ADD通过捕捉贝叶斯网络中父子节点之间的环境独立性来减少网络中的概率参数的原理,进而推导出了CPT到等价ADD转化的算法.最后,通过实例验证了ADD存储方式的有效性.结果表明,对于具有环境独立特性的贝叶斯网络,相对于CPT的存储方式,等价ADD存储方式可有效减少网络中的概率参数,为贝叶斯网络推理效率的提高提供一种有效手段.     

多状态电子装备可靠性评估方法研究

电子装备潜隐性故障是工程实践中的客观存在,正确评估多态电子装备的可靠性状态是进行潜隐性故障分析的前提.在考虑电子装备"安全-潜在故障-故障"三种工作状态的基础上,建立了多状态电子装备状态模型,提出了基于贝叶斯网络的多状态电子装备可靠性评估方法,实现了对系统可靠性状态的有效评估.实例分析表明,多状态贝叶斯网络可靠性评估模...     

信息系统安全风险的评估步骤研究绿色设计在制造企业中的作用

本文探讨了信息系统安全评估的过程与主要实施步骤，给出了一个进行信息系统安全风险评估的主要框架。在这个框架中对需要注意的评估实施主要环节作出了说明和解释，并提供了主要方法。     

基于模糊贝叶斯网络的态势威胁评估模型

针对传感器测量数据的不确定性,提出基于模糊贝叶斯网络的态势威胁评估模型,该模型首先将不确定性数据分为模糊域和概率域两大类,然后在模糊域使用模糊综合评判得到威胁目标的动态威胁度,接着运用可能性概率转换理论将模糊表示的动态威胁度转化成概率域知识,最后在概率知识域使用贝叶斯网络推理算法得到目标的威胁等级.实例计算表明,该方法能够较好的反映威胁源的威胁等级,为武器系统选择跟踪打击目标提供决策依据,具有一定的实用性.     

基于模糊概率赋值的新型贝叶斯异常检测模型

提出了一种结合模糊决策与贝叶斯方法的异常检测模型，该模型将系统中与安全相关的事件进行分类，并以模糊隶属度函数的形式给出各类事件发生异常的实时置信度。异常检测系统综合某时刻所有实时概率取值，做出贝叶斯决策。同简单使用阈值方法的贝叶斯入侵检测模型相比，采用了模糊概率赋值的贝叶斯异常检测模型，在提高对问题描述的精确性同时，由于它对多种类型安全相关事件提供支持而具有更好的适应性，可以更全面地对更复杂的系统行为进行建模。     

贝叶斯网络在运营隧道结构风险评估中的应用

运营地铁隧道结构风险复杂且影响大,如何高效评估运营地铁隧道结构风险等级,并快速辨识关键致险因素亟待解决。提出了基于模糊多态贝叶斯网络的地铁运营隧道结构风险评估方法。首先阐述了模糊多态贝叶斯网络模型的构建过程,然后根据贝叶斯原理进行地铁运营隧道结构风险因素的概率预测及重要度分析来评估系统风险。最后结合工程实例验证了该方法的适用性,为盾构地铁运营隧道结构的风险预测和防范提供了参考依据。     

Bayesian risk-based decision method for model validation under uncertainty

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment.     

Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery

In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the relationships among the model variables. By solving a bi-objective optimization problem applying the genetic algorithm (GA) with the Pareto ranking approach, the network structure was learned. Then, risk modeling was performed for a petroleum refinery focusing on HydroDeSulfurization (HDS) technology throughout its life cycle. To extend the model horizontally and make it possible to evaluate the risk trend throughout the technology life cycle, we developed a dynamic Bayesian network (DBN) with three-time slices. A two-way forward and backward approach was used to analyze the model. The model validation was performed by applying the leave-one-out cross-validation method.     

基于贝叶斯网络的项目组合风险度量

项目组合的交互效应特性使得项目组合风险不能通过单个项目风险的线性叠加获得。基于贝叶斯网络建模提出了一种项目组合风险度量的新方法。该方法通过将专家知识与K2算法相结合,求得项目组合风险的贝叶斯网络结构,并通过度量交互效应对项目风险的影响计算网络中每个节点的条件概率表,实现项目组合风险的贝叶斯网络推理。为了得到K2算法所需的有序节点输入,计算项目风险间的互信息,并基于互信息与条件独立检验求得项目节点的顺序。最后通过一个高新技术企业项目组合的应用实例说明该方法的实用性和有效性。     

Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal

The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If we take various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering specific systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, we believe that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief.In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a model for uncertainty propagation of relevant parameters were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, we could connect the results of probabilistic inference from the Bayesian network with the consequence evaluation model addressed. We got a number of practical results to improve current knowledge base for the prioritization of future risk-dominant variables in an actual site.     

A probabilistic approach for determining the control mode in CREAM

The control mode is the core concept for the prediction of human performance in CREAM. In this paper, we propose a probabilistic method for determining the control mode which is a substitute for the existing deterministic method. The new method is based on a probabilistic model, a Bayesian network. This paper describes the mathematical procedure for developing the Bayesian network for determining the control mode. The Bayesian network developed in this paper is an extension of the existing deterministic method. Using the Bayesian network, we expect that we can get the best estimate of the control mode given the available data and information about the context. The mathematical background and procedure for developing equivalent Bayesian networks for given discrete functions provided in this paper can be applied to other discrete functions to develop probabilistic models.     

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion. Moreover, it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data. Finally, a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method. The research results show that this method has strong applicability to the network security situation assessment of complex Industrial Internet systems. It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures, thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.     

数据驱动下孪生贝叶斯理论非齐次泊松过程的结构损伤评估方法

以结构件裂纹扩展过程中的损伤状态评估为研究对象,提出了一种孪生贝叶斯理论非齐次泊松过程的结构损伤评估方法.首先,结合基于裂尖场能量的可靠度序化策略与非齐次泊松过程,运用贝叶斯理论对试验信息及总体过程参数的渐进关系进行组合,获得过程参数先验分布.同时,基于裂尖场能量递进因子与似然函数的概念,通过先验信息、序化策略及后验信...     

Application of a predictive Bayesian model to environmental accounting

Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.