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.

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.

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.

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.

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.

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.

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.

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

A Subjective Risk Analysis Approach of Container Supply Chains

After the 9/11 terrorism attacks, the lock-out of the American West Ports in 2002 and the breakout of SARS disease in 2003 have further focused mind of both the public and industrialists to take effective and timely measures for assessing and controlling the risks related to container supply chains (CSCs). However, due to the complexity of the risks in the chains, conventional quantitative risk assessment (QRA) methods may not be capable of providing sufficient safety management information, as achieving such a functionality requires enabling the possibility of conducting risk analysis in view of the challenges and uncertainties posed by the unavailability and incompleteness of historical failure data. Combing the fuzzy set theory (FST) and an evidential reasoning (ER) approach, the paper presents a subjective method to deal with the vulnerability-based risks, which are more ubiquitous and uncertain than the traditional hazard-based ones in the chains.

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

In the medical field,the detection of breast cancer may be a mysterious task.Physicians must deduce a conclusion from a significantly vague knowledge base.A mammogram can offer early diagnosis at a low cost if the breasts'satisfactory mammogram images are analyzed.A multi-decision Intuitionistic Fuzzy Evidential Reasoning(IFER)approach is introduced in this paper to deal with imprecise mammogram classification efficiently.The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy.The results of the proposed technique are approved through simulation.The simulation is created utilizing MATLAB software.The screening results are classified and finally grouped into three categories:normal,malignant,and benign.Simulation results show that this IFER method performs classification with accuracy almost 95%compared to the already existing algorithms.The IFER mammography provides high accuracy in providing early diagnosis,and it is a convenient diagnostic tool for physicians.

Regression model for estimation of attribute reliability

In this paper,a regression model is developed to estimate attribute reliability in the evidential reasoning(ER)context.By analysing the difference between attribute weight and attribute reliability,a general qualitative definition of attribute reliability is provided.The reliability of an attribute is quantitatively measured in consistence with the qualitative definition in the context of the ER approach.A regression model is then constructed to generate attribute reliabilities by minimising the maximum differences between the real value of attribute reliability and its estimation.Within the post-optimal solution space of attribute reliabilities,an optimisation model is constructed to determine the expected utilities of each alternative in order to generate solutions to multiple attribute decision analysis problems.Asale place selection problem in Qingyang County of Chizhou in Anhui province of China is analysed using the proposed regression model to demonstrate its detailed implementation process,validity and applicability.

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination Belief Rule Base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.

A hierarchical analytical model for performance management of integrated logistics

In this paper,a holistic hierarchical analytical model is proposed to assess the performance of enablers in an integrated logistics system.Due to the ambiguous and complex environment,various refinements are needed to assess enablers and prioritize for the criteria such as economic,operational,and environment.The proposed hierarchical model is developed by a systematic approach that includes fuzzy analytical hierarchy process(FAHP),triangular fuzzy numbers(TFN),an evidential reasoning algorithm(ERA),and expected utility theory(EUT).The FAHP is used to analyze and obtain the weights of the criteria by considering the expert’s opinions.ERA is used to synthesize the enablers based on the selected criteria.These enablers are represented using subjective assessment along with a set of evaluation grades for a qualitative attribute.EUT helps in obtaining crisp values of enablers for their performance estimation.With these set of methodologies,a hierarchical model is proposed that prevent low flexibility and inadequate appropriateness of the proposed model.Further,the model helps in scenario generation for the logistics professionals who are facing various problems in integrating logistics and incorporating sustainability due to lack of appropriate methodologies and evaluation techniques.Finally,sensitivity analysis is used for overall model validation.

A novel approach for navigational safety evaluation of inland waterway ships under uncertain environment

This paper proposes a safety evaluation model of inland waterway ship navigation based on the fuzzy theory and evidential reasoning(ER)approach.The proposed model is a three-level hierarchical system consisting of 18 indicators that are identified and determined through literature review and expert surveys.By using fuzzy theory,the factors of the index level are converted into belief distributions,and the information of indicators within each level is merged by using the ER approach so that the evaluation of the ship’s navigation safety can be realized.This model can deal with multi-source information and both qualitative and quantitative indicators in an integrated framework.Some case studies on the safety status of ships navigating in the middle reaches of the Yangtze River during dry season are conducted to validate the feasibility and practicability of the proposed model.The results show that the risk levels obtained from the proposed model are consistent with the real-life situation to a large extent.The novel model proposed in this study provides a useful reference for maritime safety management and will help managers and decision-makers in the risk prevention and early warning of inland waterway transportation accidents.

Decentralised energy and its performance assessment models

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean,decentralised power generation.The development of decentralised energy(DE)is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future.This paper reviews the concepts,development status,trends,benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems.A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis,which considers the identification,definition and assessment grade of decision criteria.The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system.Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

Overall Efficiency and its Decomposition in a Two-Stage Network DEA Model

This paper aims to propose a new approach to decompose an overall data envelopment analysis model into equivalent two-stage models.In this approach,we use a minimax reference point method to set the weights and reliabilities of the two stage models so that the combined efficiency of the two stages is equal to the overall efficiency.The equivalent multi-stage models are useful to support planning for performance improvement.An illustrative example is first explored to compare the results from the new approach with those of four other existing approaches.The main finding from the comparisons is that the new decomposition approach of this paper satisfies the proposed assumptions.A case study is then conducted on a two-stage process of steel manufacturing to illustrate the validity and applicability of the proposed approach.