Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

A GMDA clustering algorithm based on evidential reasoning architecture

The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering based on General Mixture Decomposition Algorithm(GMDA-EC)is proposed.First,the belief classification of target cluster is carried out,and the sample category of target distribution overlapping region is extended.Then,on the basis of General Mixture Decomposition Algorithm(GMDA)clustering,the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target.Finally,the performance of the algorithm is verified by the synthetic dataset and the measured dataset.The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.

A variable weight approach for evidential reasoning

A variable weight approach was proposed to handle the probability deficiency problem in the evidential reasoning （ER） approach. The probability deficiency problem indicated that the inadequate information in the assessment result should be less than that in the input. However, it was proved that under certain circumstances, the ER approach could not solve the probability deficiency problem. The variable weight approach was based on two assumptions： 1） the greater weight should be given to the rule with more adequate information; 2） the greater weight should be given to the rules with less disparate information. Assessment results of two notional case studies show that 1） the probability deficiency problem is solved using the proposed variable weight approach, and 2） the information with less inadequacy and more disparity is provided for the decision makers to help reach a consensus.

Integration-centric approach to system readiness assessment based on evidential reasoning

An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system＇s structure. The importance of the system＇s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.

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 showthat 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.

Evidential Reasoning in Air Battle Systems

Bayesian statistics assigns basic probabilities to singletons （single element sets）. The Dempster-Shafer evidence theory generalizes Bayesian statistics by assigning basic probabilities to subsets to represent evidence and to develop evidential reasoning. This paper discusses what is the strength of evidence theory. As an application of evidence theory, evidential reasoning in air battle systems is discussed. In the air battle system, evidential reasoning is applied to fuse the muitisensor iaformation and identify the type of aircraft. The effectiveness of this fusion approach is evaluated by simulated data.

A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

半定量信息工业控制异构网络安全评估

工业控制系统的异构性及其与互联网深度融合给工控系统安全评估带来精度不高等诸多问题.本文提出一种基于半定量信息的工业异构网络安全态势评估模型,该模型可以提高评估精度,解决难以描述不确定性信息和难以契合工控异构网络复杂结构等现存问题.该方法将工业控制网络划分为不同异构子网,根据子网特点选取有针对性的评估属性,并使用性能优秀的ER规则对不同类型的评估属性进行融合,可以有效利用定性知识和定量数据,获得更加准确的评估结果.在多个实际工业控制网络数据集上进行评估实验得到的结果表明:本文提出的模型与网络安全评估实际参考值的均方根误差值比RF、BP、SVM模型分别降低了80.2%、40.8%和87.7%,验证了该模型的有效性和准确性.

融合频谱特性与聚类云-证据推理的油纸绝缘老化程度诊断

油纸绝缘系统老化程度与电力变压器的稳定运行密切相关。针对油纸绝缘频域介电谱(frequency-domain spectroscopy,FDS)所提相关特征量单一和未考虑频谱特性指标间冲突性与随机性而导致老化诊断结果误差大的问题,提出了融合频谱特性与聚类云-证据推理的油纸绝缘老化程度诊断方法。首先,根据不同老化试样的频谱特征提取老化诊断多特征指标,并利用非线性拟合构建老化特征数据库。其次,基于Kmeans聚类提出适配非线性变化指标的聚类云模型基本概率分配方法。最后,针对多特征量的冲突性及相关性差异,利用CRITIC-G1综合赋权法计算各指标对应的证据修正因子,并进行基本概率再分配,利用证据融合推理得出油纸绝缘系统真实老化程度。实验结果表明,所提方法可准确诊断复合油纸绝缘样本的老化状态,为电力检修策略的制定提供了理论指导。

刑事案件事实认定中经验法则的运用

经验法则是证据评价的依据、建立证据与事实之间联系的桥梁以及事实认定合理性的检验标准,在刑事案件事实认定中经验法则的运用不可或缺。经验法则的适当选取是其规范运用的前提和基础,对于指向一致的经验法则应以盖然性程度为选取的基准,而内容迥异的经验法则则以具体条件的符合性为选取标准。裁判文书应当展示经验法则的内容,并结合案件的事实与证据论证经验法则选取的准确性与运用的合理性;对于控辩双方援引的经验法则,裁判文书应区别情况选择回应的方式,以增强裁判结论的正当性。对于经验法则运用的错误,可以通过启动第二审程序、审判监督程序实现救济。经验法则的规范运用有赖于控辩双方运用经验法则的公开与论辩、人民陪审员制度功能的充分发挥、直接审理原则的彻底贯彻等程序支撑规则。

飞行员应急处置能力评价模型

为提高飞行员在紧急情境下的应急处置能力,减少民航安全事故,基于决策模型和应激理论模型,分析飞行员任务过程,从飞行员运行安全能力和飞行员储备安全能力2个方面建立飞行员应急处置能力指标体系;运用模糊层次分析法(FAHP)建立包含安全运行能力B1和储备安全能力B2指标体系,结合专家意见确定二级指标的隶属度,得到飞行员应急处置能力的核心指标;通过证据推理(ER)算法合成民航安全领域相关专家综合评价飞行员应急处置能力的流程,并选取某航空公司2个机组飞行员进行实证分析。研究结果表明:评价飞行员应急处置能力模型很好地降低不确定性对评价结果的影响,从而显著提高评价结果的可靠性。

不确定性多属性决策中的ER方法改进

基于对不确定性多属性决策问题中ER方法的研究,提出一种不确定性多属性决策中的改进ER方法,并证明该方法完全满足证据合成的4个公理.通过实例运算,进一步验证了新方法的有效性和合理性.

城市洪灾应急案例检索中的RIMER方法研究

为处理复杂城市洪灾应急案例检索问题,设计置信规则库,提出结合归纳索引法和基于证据推理的置信规则库推理方法(RIMER)的应急案例索引技术。设计应急案例检索框架,通过案例知识表示与转化,构建城市洪涝灾害应急管理置信规则库;通过激活权重设置、证据推理和效果评价,实现城市洪灾应急案例相似度定量评价;并通过武汉市洪灾应急处置进行算例分析。结果表明:用置信规则结构能处理洪灾应急案例中的模糊信息,用证据推理方法能快速检索存在复杂依赖关系的应急案例库。

Dempster证据合成法则的通用实现方法

证据推理是不确定性推理的一种重要方法,而Dempster合成法则是进行证据推理的核心。针对目前Demp-ster证据合成法则的算法实现不能处理合成结果的焦元为多个假设或命题的集合、近似算法的计算不够精确等问题,提出通过位向量表示识别框架子集,并利用线性表、平衡树等数据结构实现证据合成的3种精确、通用算法。理论分析与仿真实验表明,所实现的算法是有效的。

考虑作战行动的单兵系统体系贡献率评估

针对单兵系统体系贡献率评估过程中存在的未考虑作战任务及评估信息不确定性等问题,提出了基于作战行动的两级证据推理评估方法,从基于能力需求的视角进行作战任务分解和能力分析,建立了作战能力需求指标体系;建立两级置信规则库,构建作战活动与能力需求的映射矩阵,采用证据推理进行能力满足度、作战任务完成率的计算,获得了单兵系统的体系贡献率,最后基于城市作战战场想定,给出了分析实例。基于作战行动的两级证据推理评估方法,对于单兵系统体系贡献率评估具有针对性和有效性,也为装备体系贡献率评估提供了一种新思路。

一种基于D-S理论的自评系统ER改进方法

针对质量奖自评系统中评分方法的主观性问题,本文基于D-S理论,提出了一种新的证据推理方法,并证明该方法完全满足Y ang等人提出的任何证据合成过程必须满足的四个公理。通过自评实例运算,进一步验证了该方法的有效性和合理性。

基于置信规则库的干式空心电抗器状态评估方法研究

针对电力设备故障样本少且获取困难等问题,提出了基于置信规则库的干式空心电抗器状态评估方法。通过搭建多工况下干式空心电抗器电气及温升特性试验平台,获取不同工况下电抗器有功功率和最热点温升率数据样本集,建立基于置信规则库和证据推理的电抗器状态评估模型。为了减小因专家主观经验对状态评估模型预测结果的影响,提出置信规则库优化方法,并采用证据推理算法将电抗器输入特征信息转化为输出状态等级。利用测试数据对评估模型进行测试,结果验证了基于小训练样本的干式空心电抗器状态评估方法的有效性和准确性。

基于ER算法的潜艇鱼雷武器系统损伤等级评估

损伤等级评估研究对于潜艇维修计划的制定,抢修任务的分配具有十分重要的意义。本文根据武器系统指标体系和损伤评估影响因素的特点,提出了基于多属性决策ER算法的损伤等级评估模型。该模型不仅能够组合多个损伤因子的评估问题,而且能够处理由不确定信息产生的不确定性,并给出未知信息的信任度,是潜艇战场损伤等级评估的有效工具。评估结果表明,该模型能够综合考虑评估过程中的不确定性信息,对于制定科学合理的武器系统损伤处理决断具有重要意义。