An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

Improvement method for the combining rule of Dempster-Shaferevidence theory based on reliability

An improvement method for the combining rule of Dempster evidence theory is proposed. Different from Dempster theory, the reliability of evidences isn't identical; and varies with the event. By weight evidence according to their reliability, the effect of unreliable evidence is reduced, and then get the fusion result that is closer to the truth. An example to expand the advantage of this method is given. The example proves that this method is helpful to find a correct result.

A new oil spill detection algorithm based on Dempster-Shafer evidence theory

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.

THE COMBINATION PREDICTION OF TRANSMEMBRANE REGIONS BASED ON DEMPSTER-SHAFER THEORY OF EVIDENCE

Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.

基于改进DS证据理论和贝叶斯网络的海运供应链风险评估

作为一个复杂的网络组织,海运供应链为全球产业链提供运输服务,在全球化大背景下,海运供应链面临众多风险与挑战,影响供应链的安全平稳运行。因此,研究其风险至关重要。针对海运供应链风险评估体系中影响因素的不确定性和专家知识推断中存在的主观性,文章提出了基于改进DS证据理论和贝叶斯网络的海运供应链风险评估模型。结果表明,海运供应链处于较高风险等级状态,其中“信息系统风险”和“外部环境风险”概率较高,需要重点关注。该模型可以从系统的角度评估海运供应链风险,切实增强风险评估工作的科学性、准确性;评估结果可为供应链上的参与者进行风险管理提供有效参考。

Temporal evidence combination method for multi-sensor target recognition based on DS theory and IFS

In order to effectively deal with the conflict temporal evidences without affecting the sequential and dynamic characteristics in the multi-sensor target recognition(MSTR) system at the decision making level, this paper proposes a Dempster-Shafer(DS) theory and intuitionistic fuzzy set(IFS) based temporal evidence combination method(DSIFS-TECM). To realize the method,the relationship between DS theory and IFS is firstly analyzed. And then the intuitionistic fuzzy possibility degree of intuitionistic fuzzy value(IFPD-IFV) is defined, and a novel ranking method with isotonicity for IFV is proposed. Finally, a calculation method for relative reliability factor(RRF) is designed based on the proposed ranking method. As a proof of the method, numerical analysis and experimental simulation are performed. The results indicate DSIFS-TECM is capable of dealing with the conflict temporal evidences and sensitive to the changing of time. Furthermore, compared with the existing methods, DSIFS-TECM has stronger ability of anti-interference.

基于Z-number和改进DS证据理论的风险评估方法

针对任务风险难度量、评估信息不确定性强等问题,提出一种Z-number和改进DS证据理论的风险评估方法。利用Z-number方法描述评估指标,得到各风险等级的初始基本概率分配(basic probability assignment,BPA);基于信度熵和皮尔逊相关系数改进DS证据理论克服悖论问题进行信息融合,确定风险的最终等级;接着,基于信息融合结果引入Joussleme距离求解专家可信度。最后,以重装空投任务为例,验证本文所提风险评估方法的合理性,并对比分析不同改进DS证据理论方法得到的结果,验证所提方法的有效性和准确性。

基于DS证据理论的极地邮轮减摇鳍选型评价

极地邮轮工作环境特殊,减摇鳍装置的选型需考虑抗低温、抗恶劣海况等在内的诸多因素,单凭主观判断很难选出最优的减摇鳍装置。为解决该问题,提出基于层次分析法(AHP)-熵权法(EWM)、三角模糊理论以及Dempster-Shafer(DS)证据融合的综合评价方法。首先构建环境适应性、装置性能等4个一级指标以及13个二级指标的选型评价指标模型,采用组合赋权法计算定性、定量指标权重;其次构建识别框架及模糊评语集,并邀请专家组对指标做出模糊评价,利用证据距离对DS证据合成策略做出改进,将修正后证据进行两两合成从而得到各方案二级指标mass函数。最后对选型方案进行综合评价,选出符合实际需求的最优减摇鳍装置。结果表明,该方法能充分考虑决策过程的不确定性,对极地邮轮减摇鳍装置选型起到科学决策的作用。

Structural damage assessment using improved Dempster-Shafer data fusion algorithm

As an efficient tool in handling uncertain issues, Dempster-Shafer evidence theory has been increasingly used in structural health monitoring and damage detection. In applications, however, Dempster-Shafer evidence theory sometimes leads to counter-intuitive results. In this study, a new fusion algorithm of evidence theory is put forward to address various counter-intuitive problems and manage the reliability difference of the evidence. The proposed algorithm comprises the following aspects:(1) Dempster's combination rule is generalized by introducing the concept of evidence ullage. The new rule allows classical Dempster's rule and can resolve counter-intuitive problems cause by evidence conflict and evidence compatibility;(2) a reliability assessing method based on a priori and posterior knowledge is proposed. Compared with conventional reliability assessment, the proposed method can reflect the actual evidence reliabilities and can efficiently reduce decision risk. Numerical examples confirm the validity and utility of the proposed algorithm. In addition, an experimental investigation on a spatial truss structure is carried out to illustrate the identified ability of the proposed approach. The results indicate that the fusion algorithm has no strict request on the accuracy and consistency of evidence sources and can efficiently enhance diagnostic accuracy.

基于DS证据理论融合油液振动多参数的故障诊断方法研究

齿轮箱广泛应用于石油化工、船舶、电力与工程机械等领域,其故障监测诊断技术研究一直是热点问题。提出了一种基于DS证据理论融合油液与振动多参数的齿轮箱故障诊断方法,针对传统单一振动信号分析方法对齿轮磨损早期不敏感的问题,采用径向基神经网络建立振动时域特征与油液特征融合的分类模型,基于高维变分自编码器自适应提取振动频域特征并完成故障分类;将由径向基神经网络与高维变分自编码器分别得到的不同证据体通过加权DS证据理论进行融合,获得最终的诊断结果。与不同融合方法进行对比,验证了该方法对齿轮故障诊断的实际效果。

A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

Full Diversity Reception Based on Dempster-Shafer Theory for Network Coding with Multiple-Antennas Relay

In this paper,a two-way relay system which achieves bi-directional communication via a multiple-antenna relay in two time slots is studied.In the multiple access(MA) phase,the novel receive schemes based on Dempster-Shafer(D-S) evidence theory are proposed at the relay node.Instead of traditional linear detection,the first proposed MIMO-DS NC scheme adopts D-S evidence theory to detect the signals of each source node before mapping them into network-coded signal.Moreover,different from traditional physical-layer network coding(PNC) based on virtual MIMO model,the further proposed MIMO-DS PNC comes from the vector space perspective and combines PNC mapping with D-S theory to obtain network-coded signal without estimating each source node signal.D-S theory can appropriately characterize uncertainty and make full use of multiple evidence source information by Dempster's combination rule to obtain reliable decisions.In the broadcast(BC) phase,the space-time coding(STC) and antenna selection(AS) schemes are adopted to achieve transmit diversity.Simulation results reveal that the STC and AS schemes both achieve full transmit diversity in the BC phase and the proposed MIMO-DS NC/PNC schemes obtain better end-to-end BER performance and throughputs compared with traditional schemes with a little complexity increasing and no matter which scheme is adopted in the BC phase,MIMO-DS PNC always achieves full end-to-end diversity gain as MIMO-ML NC but with a lower complexity and its throughput approaches the throughput of MIMO-ML NC in high SNR regime.

A New Fusion Method for Conflicting Evidence

A new conflicting evidence fusion method is proposed for the deficiency of Dempster's rule which can not fuse the conflicting evidence. Evidence is divided into three categories:believable evidence, non-conflicting evidence and conflicting evidence. The influences of these three categories of evidences on fusion results when discounted are analyzed respectively. On these bases, the evidence distance and the conjunctive conflict are utilized in sequence to recognize the believable evidence and non-conflicting evidence. The discounting factors of these two categories of evidences are set to one, which keeps the evidences support the true hypothesis to the greatest degree, and makes the fusion results focus onto the true hypothesis. Examples of some missile fault diagnosis show that the new method can effectively fuse the conflicting evidences, and is suited to fuse the relievable evidences. The new method improves the reliability and rationality of fusion results compared with traditional methods.

基于DS–PCA模型的包装设备滚动轴承故障诊断方法研究

目的针对包装设备长期连续工作的工作特性,对其滚动轴承的常见失效故障进行分析,提出一种基于DS–PCA模型的滚动轴承故障诊断方法,构建滚动轴承DS–PCA故障诊断模型,实现包装设备中滚动轴承的在线故障诊断。方法先运用DS证据理论对采集到的滚动轴承径向振动数据和轴向振动数据进行融合,使得信息具有整体完备性,同时对采集信号进行决策规则下的去噪处理,剔除干扰噪声信息;然后利用主成分分析法(PCA)将融合后的振动信号数据进行Q与T2的统计量计算,并通过对故障轴承振动信号的Q与T2统计量计算,确立故障时的经验阈值;最后,依据实时统计量与经验阈值对比,判断滚动轴承是否发生故障。结果通过对西储大学公开的滚动轴承试验数据分析计算,得到滚动轴承故障诊断准确率达到94%。结论该方法满足包装设备故障诊断的要求,其应用将有利于提升包装企业的生产质量和效率。

Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

Cloud computing provides easy and on-demand access to computing resources in a configurable pool.The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines(VMs),instead of being restricted on a single physical server.When more and more network services are deployed on the cloud,the detection of the intrusion likes Distributed Denialof-Service(DDoS)attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system.In this paper,we propose a cloud-based intrusion detection system(IDS)which inspects the features of data flow between neighboring VMs,analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory,and eventually combines the evidence among all pairs of VMs using the method of evidence fusion.Unlike the traditional IDS that focus on analyzing the entire network service externally,our proposed algorithm makes full use of the internal interactions between VMs,and the experiment proved that it can provide more accurate results than the traditional algorithm.

尾矿库安全运行状态评价的云-DS证据理论模型

为了实现尾矿库安全运行状态的精准评估,提出了一种基于云-DS证据理论的尾矿库安全运行状态评估模型。基于文献计量法确定了尾矿库安全运行状态评估指标体系,并划分了各安全状态等级的量化范围。基于正态分布云模型获取各指标的云数字特征和各等级的确定度,并将其构建为基本概率赋值矩阵,转化为尾矿库安全运行状态评估指标的基本概率分配;提出了基于博弈论的指标权重确定方法,结合尾矿库安全运行状态评估指标基本概率分配得到的证据进行证据融合,得到尾矿库不同安全状态等级的基本概率分配结果,依据最大隶属度原则,确定相对应的安全状态等级作为最终证据融合评估结果。经尾矿库现状实例验证,模型的评估结果与尾矿库的实际安全状态相符。验证结果表明模型的准确性较高,对于尾矿库的安全状态评估具有很好的适用性,可为尾矿库相关安全管理政策的制定提供理论依据。

一种应用DS证据理论的雷达目标与IFF点迹关联方法

针对目标数量多、目标构成复杂环境下雷达目标与敌我识别(Identification Friend or Foe,IFF)点迹关联不准确的问题,提出了一种基于DS(Dempster-Shafer)证据理论的关联方法。基于区间灰数模型完成雷达目标与IFF点迹的灰关联度计算,并据此生成DS证据理论中辨识框架的基本概率赋值;利用Dempster规则对证据进行组合,当证据之间存在冲突时采用改进Murphy方法对数据进行处理;最终通过概率转换方法完成关联判决,形成对目标敌我属性的判定。典型场景下的仿真结果表明,该方法能够实现雷达目标与IFF点迹的有效关联,通过多次询问及关联过程,可提升不同场景下的关联正确率。

THE PROBABILITY HYPOTHESIS DENSITY FILTER WITH EVIDENCE FUSION

The original Probability Hypothesis Density (PHD) filter is a tractable algorithm for Multi-Target Tracking (MTT) in Random Finite Set (RFS) frameworks. In this paper,we introduce a novel Evidence PHD (E-PHD) filter which combines the Dempster-Shafer (DS) evidence theory. The proposed filter can deal with the uncertain information,thus it forms target track. We mainly discusses the E-PHD filter under the condition of linear Gaussian. Research shows that the E-PHD filter has an analytic form of Evidence Gaussian Mixture PHD (E-GMPHD). The final experiment shows that the proposed E-GMPHD filter can derive the target identity,state,and number effectively.

基于DST的调相机转子轻微匝间短路故障诊断

转子绕组匝间短路故障是调相机的一种常见故障,一旦出现就会直接影响调相机在系统中的无功输出。由于调相机在运行过程中时常要对无功功率进行调节,仅用单独的特征量难以准确地对故障进行诊断,极易出现误判。针对此问题,提出一种基于DS证据理论(DS evidence theory,简称DST)的调相机转子轻微匝间短路故障的诊断方法,综合利用振动信号和励磁电流中的感应谐波对故障进行诊断。首先,通过随机森林计算单独特征量下的故障概率;其次,通过DST的组合规则将二者的结果进行综合诊断;最后,通过实验进行验证。实验结果表明,该方法诊断的正确率均高于单独特征量的诊断,可以有效提高调相机转子轻微匝间短路故障识别的准确性。

Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic

A novel Federated learning classifier is proposed using the Dempster-Shafer(DS)theory for the set-valued classification of industrial IoT Distributed Denial of Service(DDoS)attack traffic.The proposed classifier,referred to as the evidence-based federated learning classifier,employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service(DDoS)traffic from the local data of private industrial clients.The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer,situated on the federated server.Lastly,the set value classification task of attack mode is executed in the expected utility layer.A learning strategy is proposed to update the network parameters in a joint manner according to the weight of mass function value.Experiments on DDoS traffic classification tasks in CIC-DDoS2019 datasets prove that Federated learning,DS layer,and expected utility layer is effective in enhancing the multi-class classification accuracy,especially for those DDoS mixed attack traffic.