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%.

A Digital Evidence Fusion Method in Network Forensics Systems with Dempster-Shafer Theory

Network intrusion forensics is an important extension to present security infrastructure,and is becoming the focus of forensics research field.However,comparison with sophisticated multi-stage attacks and volume of sensor data,current practices in network forensic analysis are to manually examine,an error prone,labor-intensive and time consuming process.To solve these problems,in this paper we propose a digital evidence fusion method for network forensics with Dempster-Shafer theory that can detect efficiently computer crime in networked environments,and fuse digital evidence from different sources such as hosts and sub-networks automatically.In the end,we evaluate the method on well-known KDD Cup1999 dataset.The results prove our method is very effective for real-time network forensics,and can provide comprehensible messages for a forensic investigators.

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.

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.

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.

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.

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。

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.

A NEW METHOD FOR CONSTRUCTING FUZZY EVIDENCE BASED ON THE NON-CONSONANT RANDOM SET

Natural-language information is often mathematically expressed by fuzzy sets. With the random set theory as a bridge, this kind of information can be transformed into fuzzy evidence in Dempster-Shafer (DS) theory. Then Dempster's combination rule or other combination rules of evi- dence can be used perfectly for fusing natural-language and other information. However, this traditional transformation involves the use of α -cutsets to construct the focal elements which have to be repre- sented as consonant sets. This construction is very inflexible and unreasonable in some practical ap- plications. In this paper, with the desire to overcome this limitation, a method for constructing more general non-consonant focal elements is proposed based on the random set theory. Some examples are given to show the generality and the efficiency of this new method. Finally, we validate that non-consonant constructions provide less degrees of total uncertainty than that of the consonant case in these examples by using the evaluation criterion of total uncertainty.

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.

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证据理论与综合赋权改进可拓法的黄土区重载铁路高填方路基服役性能评价

为有效评价黄土区重载铁路高填方路基的变形与稳定状态,在重载铁路高填方路基服役性能初选评价指标库基础上,通过基于证据可信度的DS证据理论融合指标的重要度、独立度与区分度3项特征值,构建重载铁路高填方路基服役性能优选指标体系;基于综合赋权改进可拓评价法,建立针对重载铁路高填方路基服役性能的多层次综合评价模型,并利用该模型对大准及巴准铁路两段高填方路基服役性能进行评价。研究结果表明,上述两段高填方路基服役性能等级评价结果依次为差与良,并均通过了有效性检验;本文研究所提方法较云模型更能准确评价高填方路基服役性能,且比《铁路路基大维修规则》更能全面客观地反映高填方路基服役性能所处状态。研究结果可为黄土区重载铁路高填方路基服役性能状态评价提供参考。

AN APPROACH FOR EVIDENCE CLUSTERING USING GENERALIZED DISTANCE

When evidence comes from multiple events they should be handled independently, and it is unknown to which event a piece of evidence is related. In this paper, the problem of clustering all pieces of evidence is analyzed systematically to separate them into subsets for each event on the basis of considering the describing format of evidence and making full use of the distance of evidence. An approach for evidence clustering using distance of evidence is presented based on the criterion for clustering. In the proposed approach, the method which is used to establish the initialization of clus- tering is discussed in detail, called an improved optimal distance. And the centroid vector of evidence and the clustering process are developed respectively to obtain the performance of this novel approach. Finally, an illustrative example shows that this approach is feasible and effective.

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.

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.

基于盲源分离与改进BP-DS证据理论的生猪声音多特征融合识别

为了提高多种声音混合环境下生猪惊骇声、咳嗽声与饥饿声的识别正确率,试验采用基于盲源分离与改进BP-DS证据理论的生猪声音多特征融合识别方法对生猪混合声音识别进行研究。首先,对多个拾音器采集到的混合声音进行能量检测,排除安静环境及微弱环境噪声下声音的干扰,然后利用基于快速独立成分分析(fast independent component analysis,FastICA)的盲源分离方法对采集到的混合声音进行分离;其次,提取分离后声音信号的梅尔倒谱系数及其一阶差分、线性预测倒谱系数及其一阶差分、梅尔倒谱系数与线性预测倒谱系数的组合参数,训练3种特征参数对应的BP神经网络模型;最后,分别利用训练好的BP神经网络模型对生猪声音进行识别,通过改进DS证据理论对识别结果进行融合后输出。结果表明:研究提出的盲源分离方法分离出的生猪声音信号与生猪声音源信号的相似系数均在0.99以上,可以有效地分离生猪混合声音信号;经过多特征融合后正常环境下的识别正确率达到94.22%,较单特征的正确率有一定的提高。说明基于盲源分离与改进BP-DS证据理论的生猪声音多特征融合识别方法可以更高效地识别多种声音混合环境下的生猪声音。

基于DS-DBN多测点的工业机器人RV减速器故障诊断分析

为了提高减速器故障识别能力,融合DS证据理论和深度信念网络(DBN)设计了一种基于DS-DBN多测点的故障诊断方法,并开展实验测试验证。研究结果表明:经过30代训练后达到收敛状态,训练四个测点的DS-DBN故障诊断模型分类误差均小于5%,该方法能够快速获得全局最优参数。相比较单点测试,通过DS证据理论完成测点故障诊断融合处理后,可以将故障诊断准确率提升至接近100%的程度。该研究有助于解决工业机器人RV减速器隐藏的问题,延长使用寿命、减少经济成本。

Using the Dempster-Shafer Theory of Evidence to Rank Documents

Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,