A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

An EEGA-Based Bayesian Belief Network Model for Recognition of Human Activity in Smart Home

With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).

Developing a Bayesian belief network model for prediction of R&D project success

The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always been considerably risky;however,managing Research and Development(R&D)project risks has become even more important given today’s tight schedules and limited resources.Risk management has to be an integral part of the development process.The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project.A risk quantification model based on the Bayesian belief network is proposed,which is effective in capturing the interaction between various risk factors.The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.

Data learning and expert judgment in a Bayesian belief network for aiding human reliability assessment in offshore decommissioning risk assessment

Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity.The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling.The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment.Initial analysis from well workover data,of a 5-node BBN provided insights on two different levels of severity of an accident,the’Accident’and’Incident’level,and on its respective profiles of the initiating events and the investigation-reported human causes.The initial results demonstrate that the data learnt from the database can be used to structure the BBN,give insights on how human reliability pertaining to well activities can be modelled,and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes.It is also proposed that the integrated treatment of various sources of information(database and expert judgement)through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

A Bayesian belief network approach for mapping water conservation ecosystem service optimization region

Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using Bayesian belief networks not only helps develop a better understanding of water conservation processes but also increases the rationality of scenario design and pattern optimization. This study establishes a water conservation network model. The model, based on Bayesian belief networks, forecasts the distribution probability of the water conservation projected under different land use scenarios for the year 2050 with the CA-Markov model. A key variable subset method is proposed to optimize the spatial pattern of the water conservation. Three key findings were obtained. First, among the three scenarios, the probability of high water conservation value was the largest under the protection scenario, and the design of this scenario was conducive to the formulation of future land use policies. Second, the key influencing factors impacting the water conservation included precipitation, evapotranspiration and land use, and the state set corresponding to the highest state of water conservation was mainly distributed in areas with high annual average rainfall and evapotranspiration and high vegetation coverage. Third, the regions suitable for optimizing water conservation were mainly distributed in the southern part of Maiji District in Tianshui, southwest of Longxian and south of Weibin District in Baoji, northeast of Xunyi County and northwest of Yongshou County in Xianyang, and west of Yaozhou District in Tongchuan.

Bayesian belief-based model for reliability improvement of the digital reactor protection system

The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

Assessing Chemical Mixtures and Human Health: Use of Bayesian Belief Net Analysis

Background: Despite humans being exposed to complex chemical mixtures, much of the available research continues to focus on a single compound or metabolite or a select subgroup of compounds inconsistent with the nature of human exposure. Uncertainty regarding how best to model chemical mixtures coupled with few analytic approaches remains a formidable challenge and served as the impetus for the study. Objectives: To identify the polychlorinated biphenyl (PCB) congener(s) within a chemical mixture that was most associated with an endometriosis diagnosis using novel graphical modeling techniques. Methods: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18 - 40 years who underwent a laparoscopy or laparotomy between 1999 and 2000;79 (94%) women had serum concentrations for 68 PCB congeners quantified. Adjusted odds ratios (AOR) for endometriosis were estimated for individual PCB congeners using BBN models. Results: PCB congeners #114 (AOR = 3.01;95% CI = 2.25, 3.77) and #136 (AOR = 1.79;95% CI = 1.03, 2.55) were associated with an endometriosis diagnosis. Combinations of mixtures inclusive of PCB #114 were all associated with higher odds of endometriosis, underscoring its potential relation with endometriosis. Conclusions: BBN models identified PCB congener 114 as the most influential congener for the odds of an endometriosis diagnosis in the context of a 68 congener chemical mixture. BBN models offer investigators the opportunity to assess which compounds within a mixture may drive a human health effect.

一种基于Bayesian信念网络的客户行为预测方法

提出一种基于Bayesian信念网络(BN)的客户行为预测方法.通过知识学习构建客户行为Bayesian网络(CBN),根据CBN对预实例计算联合分布概率,准确预测了一对一营销优化中的客户行为.CBN学习算法包括连线和定向部分,复杂度为O(N4)条件相关测试.在零售行业一对一营销实际应用表明,CBN学习算法较现有BN学习算法更快构建CBN,预测精度高于朴素Bayesina分类法.

基于Bayesian网络的软件开发模型

在这篇论文中,阐述了用贝叶斯信任网络(Bayesian Belief Networks:BBN)进行软件建模的方法,提出了基于BBN软件开发模型,该模型能够表示软件过程的主要活动,给出了如何构建BBN开发模型的步骤,在定义要求控制和计划的关键工作流时该模型能支持专家意见,这种模型能够应对软件开发过程的迭代特性,并对开发过程中的每一步都会渐近产生精确评估,根据其结构可对每一个工作流的整体结果做出评估。

Prioritizing Indicators for Rapid Response in Global Health Security:A Bayesian Network Approach

This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

邮轮内装物资物流集配风险评估

为量化邮轮内装物资物流集配风险并科学配置管控资源,结合置信规则库和贝叶斯网络,用于解决具有不确定性和模糊性的风险评价信息.辨识影响邮轮内装物资物流集配的关键风险因素,多维度细化风险参数表达,基于风险参数结构及权重,建立包含置信度的规则库表示风险参数与风险状态之间的对应关系;融合模糊评价数据,利用贝叶斯推理技术,得出风险因素在风险状态上的置信度分布,引入效用函数实现概率值向精确值的转换,并得到风险因素的排序结果;通过敏感性分析验证该模型的逻辑性、适用性和准确性.结果表明:邮轮内装物资物流集配风险排序位列前三的分别为内装总包商对供应商及物流服务商监管不善、参与主体权责划分不明确、以及仓储设施不满足物资存放要求.

喀斯特山区生态系统服务权衡关系分异特征及生态安全格局识别——以贵州省为例

本文运用相关模型和方法对区域2000—2020年植被净初级生产力、土壤保持服务和产水服务进行评估,并借助均方根误差和空间自相关分析探索生态系统服务权衡关系的时空分异规律,构建贝叶斯网络解析生态系统服务权衡变化的驱动因素,并设置多情景实现区域生态安全格局识别。结果表明:研究时段内3类生态系统服务均呈现出了一定增长趋势,但局部地区权衡冲突明显。不同背景条件下生态系统服务权衡关系异质性特征显著。其中,产水服务与土壤保持服务、植被净初级生产力间的权衡关系在不同高程上均强于植被净初级生产力与产水服务;土壤保持服务与植被净初级生产力、产水服务的权衡关系随坡度增加而增强;岩溶峡谷、岩溶盆地上土壤保持服务与产水服务间的冲突更明显;生态工程修复区产水服务与植被净初级生产力、土壤保持服务的权衡关系更强。生态系统服务权衡关系受多种因素影响且主导因素不尽相同,造林面积、实际蒸散发和降水总量分别是造成土壤保持服务与植被净初级生产力、土壤保持服务与产水服务、植被净初级生产力与产水服务权衡变化的主要因素。贵州省南部的望谟县和罗甸县以及东北部的江口县和印江县面临着最大的生态风险,未来可以通过调整关键变量的关键状态来提升这类区域的生态安全水平。

平原圩区水系结构与功能特征及其影响机制——以昆山南部为例

明晰平原河网水系结构和功能的影响机制对区域生态可持续发展具有重要意义。以昆山市南部防洪分区为例,选取水系指标分析圩区单元尺度下的水系网络结构与调蓄功能,并构建贝叶斯网络模型,综合考虑用地、自然、工程与政策管理等因子及其相互作用,定量探究水系结构与功能的影响机制。结果表明:(1)水系网络结构具有显著空间异质性,且水系调蓄功能与结构特征密切相关,较复杂的水系形态结构往往表现出较强的调蓄功能;(2)政策、工程、用地和自然条件等因子对水系调蓄功能的影响强度依次减弱;(3)识别水系功能优化目标下的关键变量与关键状态子集,可从社会⁃生态协同视角指导圩区单元的水系治理与优化。研究结果可为平原圩区水系网络健康与可持续发展提供理论参考和决策依据。

一种无监督双层DBN的轴承故障智能诊断方法

大型滚动轴承设备的运行环境复杂多变,以往利用模式识别建立的诊断方法,通常难以有效解决数据含有噪声,不完备、无标签等问题。因此提出一种无监督双层深度信念网络(DBN)的滚动轴承故障智能分类与诊断方法。方法利用DBN的逐层贪婪学习来挖掘与故障相关的特征信息并输入分类器。通过自适应模糊C均值聚类算法,识别未知数据中的异常值。若异常值密度聚集度低,则判定其为噪声,并以此消除分类过程噪声干扰;若异常值密度聚集度高,则判定其为一个新类别,并合并到故障知识库中。之后再将贝叶斯分类器的方法应用于二级DBN网络中,使故障损伤等级实现无监督学习。利用西储大学滚动轴承实验平台数据对此套方法进行验证,结论表明在有噪声和不完备数据建模情况下,可以很好地完成故障类型与损伤等级的准确划分,具有一定的智能性。

无线传感器网络混合恶意节点检测方法研究

无线传感器网络(wireless sensor networks,WSN)在环境监测、医疗保健等多个领域的应用越来越广泛.然而,WSN中的传感器节点易受安全威胁,尤其是恶意节点发起的不诚实推荐攻击,可能会破坏通信完整性.因此,对WSN中的恶意节点进行检测显得尤为重要.尽管近年来基于信任管理的恶意节点检测方法不断涌现,以增强WSN的安全性,但现有研究往往忽视了数据一致性及信任评估中对参与节点的持续评估,这在一定程度上限制了检测方法的有效性.针对这些问题,提出了一种融合了模糊信任模型(fuzzy trust model,FTM)算法和贝叶斯信念估计(Bayesian belief estimation,BBE)方法的WSN恶意节点检测新技术.其核心在于通过FTM算法考量数据随时间的关联性确定直接信任值,并通过BBE方法基于推荐节点的先验信任概率评估间接信任值的可信性.通过模拟实验对所提方法的有效性进行了验证,结果证明,该模型在检测WSN中的恶意节点方面相较于现有技术,具有更高的检测率和更低的误报率.

基于贝叶斯统计推理的故障定位实验研究

故障定位的目的是帮助程序员寻找引发失效的原因或故障位置,以加快调试过程.故障和失效间的关系往往非常复杂,难以直接描述故障到失效的转化.最新的研究多采用差异分析的方法,基于可疑模式,构建故障推理贝叶斯网络,其节点由可疑模式及组成可疑模式方法的调用者构成;定义了贝叶斯网络的构建算法、各个相关概率的定义及BBN中各个边的条件概率计算公式.提出基于该BBN的推理算法,推理得到包含故障的模块,并计算得到每个模块包含故障的概率.提出了评价方法,详细设计了参数调整与定位性能的关系实验和定位结果分析实验.实验数据表明,该故障定位方法取得了平均0.761的定准率和0.737的定全率,定位结果良好,具有较高的实用价值.

食品中微生物危害的风险评估建模方法改进与应用

为了解决目前食品中微生物危害风险评估中模块化过程风险模型仅能评估当前风险而未能考虑流通领域的风险因素和危害溯源等缺陷,该文对食品中微生物危害的定量风险评估建模方法进行了改进。改进方法将操作环境、人员、设备等风险因素抽象为危害转移模块,设置控制模块表征控制措施对风险因素的控制作用,设置效益模块表征实施控制措施的成本和收益;采用贝叶斯网络模型结构,结合预测微生物学,通过贝叶斯推理估计食品处理过程中微生物危害的数量及其出现的概率。仿真分析表明,改进方法在实现食品中微生物危害风险评估的同时,在同一模型结构下能溯源危害被引入的风险因素源头,评估风险因素对食品产品安全的影响程度,管理者通过综合考虑控制效果和成本能够选择合适的风险控制措施。改进的风险评估建模方法对现有方法进行了补充,扩展了风险评估模型的功能,也为企业在生产流通过程中预防和管理安全风险提供有力的工具,具有重要的理论和应用价值。

多状态机械系统可靠性的离散化建模方法

针对传统的基于二态逻辑的可靠性评估方法应用于多状态系统理论和实际应用存在差异的问题,根据贝叶斯信念网(BBN)具有双向不确定性推理功能和图形化显示的特点,提出了一种多状态机械系统可靠性离散化建模方法.首先确定BBN的节点及离散系统各元件的多个状态,并给出各状态的概率,用概率分布表(CPD)描述元件各状态之间的关系来表达关联节点的状态,最终建立离散化BBN模型.该模型避免了已有方法复杂的公式计算,对元件数量没有限制.实例分析表明了应用BBN离散化模型进行多状态机械系统可靠性评估的有效性和优越性.