Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Bayesian Networks for Groundwater Quality Assessment

The quality of groundwater resources needs to be assessed and monitored to ensure sustainable use and management.Groundwater-related data are characterized by inaccurate,missing values and significant uncertainties;whose sources range from inadequacies and errors in the measuring techniques to insufficient sampling times and frequencies.Uncertainties

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband(MBB)networks are expanding rapidly to deliver higher data speeds.The fifth-generation cellular network promises enhanced-MBB with high-speed data rates,low power connectivity,and ultralow latency video streaming.However,existing cellular networks are unable to perform well due to high latency and low bandwidth,which degrades the performance of various applications.As a result,monitoring and evaluation of the performance of these network-supported services is critical.Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users.This paper proposes a Bayesian model to estimate the minimum opinion score(MOS)of video streaming services for any particular cellular network.The MOS is the most commonly used metric to assess the quality of experience.The proposed Bayesian model consists of several input data,namely,round-trip time,stalling load,and bite rates.It was examined and evaluated using several test data sizes with various performance metrics.Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network.The proposed Bayesian network obtained a remarkable overall accuracy of 90.36%and outperformed the neural network.

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the cir- cumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian net- work of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly pro- portionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The inte- gration ofbigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

基于Bayesian网的蔬菜质量安全追溯模型构建

蔬菜产品的质量安全是当前亟待解决的问题,只有从蔬菜的种植源头抓起,找出蔬菜生产过程中影响蔬菜质量安全的关键因素,结合蔬菜生产流程,建立完善的可追溯模型,才能真正实现蔬菜生产的质量安全监控。为此,建立了基于Bayesian网的蔬菜质量安全追溯初始模型,通过该模型可兼顾"事先"预防和"事后"追踪,从而保障蔬菜的质量安全。

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

Quality-related locally weighted soft sensing for non-

Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables.Specifically,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement algorithm.The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail.The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column.It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Context-aware end-to-end QoS diagnosis and guarantee based on Bayesian network

A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorithm for discretizing continuous numeric-values is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with K2 Bayesian network （BN） structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. Then, BN inference can be carried out on the BN. Finally, the QoS metric is guaranteed to a specific value with certain probability by tuning its causal contexts to suitable values suggested by the BN inference. Our approach is validated to be sound and effective by simulations on a peer-to-peer （P2P） network.

Classification of Web Services Using Bayesian Network

In this paper, we employed Na?ve Bayes, Markov blanket and Tabu search to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes are treated as criteria, to classify web services. From the experiments, we conclude that Na?ve based Bayesian network performs better than other two techniques comparable to the classification done in literature.

基于Bayesian Network的工程质量风险管理研究

在项目的全生命周期中影响质量的不确定因素很多,本文研究工程质量风险管理,在对引起工程质量问题的各种风险因素调查研究的基础上,提出了风险识别与风险评估的流程;基于Bayesian Network的风险管理预测框架,构建工程质量风险管理的Bayesian Network模型,并选出最优的风险管理方案,可为大型工程项目质量风险管理提供一种有效、方便的定量预测管理方法。

基于贝叶斯结构方程模型的质量管理与企业创新关系研究

创新已成为企业构筑核心竞争力的重要策略,现有研究将质量管理应用于创新过程以提供创新效率,但质量管理与企业创新的关系仍存在模糊性。基于企业能力理论,本研究引入新产品开发能力作为中介变量,评估贝叶斯网络和结构方程模型因果关系处理的等价性并进行潜在类分析,构建质量管理实践与创新的贝叶斯网络模型,深入探讨质量管理对企业创新的影响机理及其非线性关系。研究发现,基础质量管理实践和核心质量管理实践对企业创新绩效的影响具有显著的差异性,基础质量管理实践、核心质量管理实践与新产品开发能力和企业创新绩效间存在明显的非线性关系。研究结论一方面检验了质量管理实践对创新的影响机理,以及不同类型质量管理实践对创新绩效影响的差异性;另一方面验证了质量管理实践与创新绩效间的非线性关系。

基于贝叶斯网络理论的空气质量分析与预测

为科学构建空气质量监测体系,提升空气质量预测精度,基于海量监测数据的不确定性,提出了一种基于贝叶斯网络的方法来预测空气质量指数AQI及相应的等级。以合肥市为研究对象,首先利用朴素贝叶斯分类算法来预测空气质量等级,训练得到待验样本的分类准确率为85%,由于该算法的条件独立性假设过于严格,进一步引入贝叶斯网络模型实证研究,基于后验概率分布训练得到预测结果。仿真实验表明待验样本的AQI预测平均绝对百分比误差为6.89%,空气质量等级分类准确率为90.28%,说明贝叶斯网络具有良好的预测效果,能为空气质量预测预报提供技术支撑,助力城市空气质量改善。

基于知识图谱推理的热轧带钢产品质量缺陷追溯

热轧带钢生产面临多工况、机理复杂、工艺参数繁多等问题,造成专家很难及时有效给出生产中导致质量缺陷的原因。由此提出一种基于知识图谱推理的质量缺陷追溯方法。首先通过可解释方法SHAP实现对随机森林模型预测结果的解释,并通过知识图谱将数据挖掘结果与工艺机理、专家经验等知识进行融合,进一步将图谱中表示工艺参数与质量参数依赖关系的子图映射到贝叶斯网络,推断不同工艺参数导致产品质量缺陷的后验概率。实际生产数据验证表明,针对不同生产工况,该方法能有效识别各个批次中导致质量缺陷的工艺参数,表现出良好识别率。

基于知识图谱的固体推进剂质量缺陷根因变量识别方法研究

针对固体推进剂生产过程中机理复杂、工艺参数众多,导致质量缺陷频发的问题,提出了一种基于知识图谱的固体推进剂质量缺陷根因变量识别的方法。首先通过历史数据挖掘对质量缺陷进行预测,并通过SHAP模型对预测结果进行具体解释。再将数据挖掘结果、专家经验与工艺机理进行结构化表示与融合,建立固体推进剂质量缺陷领域的知识图谱。最后将图谱中包含参数信息和结构关系的子图映射到贝叶斯网络中进行参数学习,从而推断出不同特征参数导致推进剂存在质量缺陷的后验概率。最终通过实际生产数据样本进行验证,结果表明:该方法能有效识别生产过程中造成固体推剂质量缺陷的根因变量,准确识别率可达85%。

智能手机组装线质量-可靠性耦合建模与评估方法

为研究多装配阶段因素因果关联致使产线良率传导与形成的机理,以实验室手机组装数字孪生产线为原型测试平台,围绕该平台锁螺丝、点胶两大工艺段良率问题,从因果关联关系出发,提出智能手机组装工艺过程产品质量-设备可靠性耦合建模方法,构建考虑多阶段因果要素关联的动态贝叶斯网络(dynamic Bayesian network,DBN)模型,并进行影响最终良率形成的溯因分析与重要度评价。所提方法的可用性和有效性在手机组装数字孪生产线上获得了测试和验证,所提方法可为产线良率损失阻隔机制、产线预防性维护决策提供性能评估支撑。

面向水质分类的分组降维核朴素贝叶斯模型

为提高贝叶斯模型在实际数据中处理相关性维度时的效率、精度,并保持其可解释性,本文提出一种改进的核朴素贝叶斯模型。首先通过关联规则挖掘相关维度子集,然后对这些子集进行降维处理,利用降维后的数据构建核朴素贝叶斯模型。实际应用的结果显示,该模型减少了降维对数据的影响,在保留有效信息的同时,提高了模型的可解释性。

基于多源数据的水利工程质量风险方法评估

获取全面、准确和及时的多源数据是水利工程质量风险评估的基础,考虑到风险事故致因的关联性与整体性会影响评估结果的可靠性和真实性,依托浙江省32项重点水利工程案例作为多源数据基础,基于文献调查和工程实例分析初步确定其风险因素,并从原材料质量、工程实体质量、行为管理质量3方面选取导致质量风险的致因变量。通过分析变量间的相关关系,利用贝叶斯网络软件GeNle构建基于水利工程质量风险评估的贝叶斯网络模型(BN),通过变量敏感性和事故最大致因链分析发现,关键因素风险路径为参建人员资格审查→资质资格→行为管理质量→水利工程质量高风险;而一般单位关键单元工程自检优良率、参建人员资格审查通过、进场材料质量合格、施工资料留存完整及阶段工程验收通过敏感度最高,其变化更容易导致事故的发生,也是影响水利工程质量的重要因素。其研究方法和成果可为水利工程质量风险控制和安全管理提供科学依据。

改进的计划评审技术在大型复杂项目进度管理中的应用

针对大型复杂项目进度管理中存在的问题,提出了PERT BN模型,其基于贝叶斯网络(BN)而改进的PERT,用于项目进度分析、控制及优化。给出了PERT BN的基本定义,并详述了其包含的节点、有向边和条件概率。然后,以某型号研制项目实验网络图为例,建立起对应的PERT BN模型实例。最后,以模型实例为基础,分析了人、成本和技术等外界因素对工序持续时间的影响,以及总工期变化时对资源的需求变化情况。