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 variable weight approach for evidential reasoning

一条可变重量途径被建议在证据的推理处理概率缺乏问题(嗯) 途径。概率缺乏问题显示在评价结果的不适当的信息应该是不到那在输入。然而，在某些情形下面， ER 途径不能解决概率缺乏问题，这被证明。可变重量途径基于二个假设：1 ) 更大的重量应该与更足够的信息被给规则；2 ) 更大的重量应该与不太迥异的信息被给规则。二概念的案例研究的评价结果显示出那 1 ) 概率缺乏问题用建议可变重量途径被解决，并且 2 ) 有更少的机能不全的信息和更多的不同被提供决定制造者帮助到达一致。

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 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.

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 multisensor information and identify the type of aircraft. The effectiveness of this fusion approach is evaluated by simulated data.

On the evidential reasoning rule for dependent evidence combination

Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.

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.

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.

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.

Performance evaluation of complex systems using evidential reasoning approach with uncertain parameters

The composition of the modern aerospace system becomes more and more complex.The performance degradation of any device in the system may cause it difficult for the whole system to keep normal working states.Therefore,it is essential to evaluate the performance of complex aerospace systems.In this paper,the performance evaluation of complex aerospace systems is regarded as a Multi-Attribute Decision Analysis(MADA)problem.Based on the structure and working principle of the system,a new Evidential Reasoning(ER)based approach with uncertain parameters is proposed to construct a nonlinear optimization model to evaluate the system performance.In the model,the interval form is used to express the uncertainty,such as error in testing data and inaccuracy in expert knowledge.In order to analyze the subsystems that have a great impact on the performance of the system,the sensitivity analysis of the evaluation result is carried out,and the corresponding maintenance strategy is proposed.For a type of Inertial Measurement Unit(IMU)used in a rocket,the proposed method is employed to evaluate its performance.Then,the parameter sensitivity of the evaluation result is analyzed,and the main factors affecting the performance of IMU are obtained.Finally,the comparative study shows the effectiveness of the proposed method.

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。

General Algorithms for Barnett's Structure in Evidential Reasoning

A major problem encountered when applications of the Dempster-Shafer ev-idence handling methods are contemplated is the computational complekity of the basic operations. Barnett has proposed a linear-time computational tech-nique for use in evidential reasoning. However, it is restricted to the entire orthogonal sum of dichotomous evidential functions. This means that there has to be a piece of evidence for each contender for the choice being made. This paper presents more general algorithms for combining dichotomous evidelitial functions. The idea is based on the fact that dichotomous evidential functions generalize simple evidential functions, and a useful general formula for com-bining simple evidential functions is available. It is therefore natural to seek general formulae or algorithms for combining dichotomous evidential functions.

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。

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。

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。