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 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.

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%.

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.

SAR images classification method based on Dempster-Shafer theory and kernel estimate

To study the scene classification in the Synthetic Aperture Radar （SAR） image, a novel method based on kernel estimate, with the Maxkov context and Dempster-Shafer evidence theory is proposed. Initially, a nonpaxametric Probability Density Function （PDF） estimate method is introduced, to describe the scene of SAR images. And then under the Maxkov context, both the determinate PDF and the kernel estimate method axe adopted respectively, to form a primary classification. Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification. Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification. Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results. Experimental results on real SAR images illustrate a rather impressive performance.

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.

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.

Triggered Asynchronous Cooperative Spectrum Sensing Scheme Based on Dempster-Shafer Theory

Aiming at solving the problems such as time consuming and application limiting presented in the existing synchronous cooperative spectrum sensing schemes,a triggered asynchronous scheme based on Dempster-Shafer(D-S) theory was proposed.Sensing asynchronously,each cognitive user calculated the confidence measure functions with double threshold spectrum sensing method.When the useful report was received by the fusion center,a fusion process would be triggered.Then the sensing results were fused together based on D-S theory.The analysis and simulation results show that the proposed scheme can improve the spectrum sensing efficiency and reduce the calculation amount of the fusion center compared with the existing schemes.

Application of the Dempster-Shafer Theory to the Classification of Pixels from Aster Satellite Images and Spectral Indices

In this paper, it is proposed to apply the Dempster-Shafer Theory (DST) or the theory of evidence to map vegetation, aquatic and mineral surfaces with a view to detecting potential areas of observation of outcrops of geological formations (rocks, breastplates, regolith, etc.). The proposed approach consists in aggregating information by using the DST. From pretreated Aster satellite images (geo-referencing, geometric correction and resampling at 15 m), new channels were produced by determining the spectral indices NDVI, MNDWI and NDBaI. Then, the DST formalism was modeled and generated under the MATLAB software, an image segmented into six classes including three absolute classes (E,V,M) and three classes of confusion ({E,V}, {M,V}, {E,M}). The control on the land, based on geographic coordinates of pixels of different classes on said image, has made it possible to make a concordant interpretation thereof. Our contribution lies in taking into account imperfections (inaccuracies and uncertainties) related to source information by using mass functions based on a simple support model (two focal elements: the discernment framework and the potential set of belonging of the pixel to be classified) with a normal law for the good management of these.

Application of Dempster-Shafer theory in mineral resource potential mapping

The Dempster-Shafer theory has been successfully applied to mineral resource potential mapping in GIS environmental. In this applied form, basic probability assignment and combined basic probability assignment are applied to measuring map pattem and map pattem combination, respectively; and the environment composed of the only two singleton sets （deposit set and non-deposit set）, is used for expressing the entire map area. For a subarea in which the certain map pattern combination exists, the combined basic probability assignment corresponding to the map pattern combination existing in this subarea, expresses the belief of inferring the subarea belonging to the deposit set from the evidence that the corresponding map pattern combination existing in the subarea. Thus, it may be served as a statistical index measuring the relative mineral resource potentials of the subarea. And it may be determined like 1） dividing the map area into a series of small equal-sized grid cells and then select the training sample set composed of the well-known grid cells or the entire grid cells; 2） estimating the basic probability assignments corresponding to each map pattern fromthe training sample set; 3） determining the map pattern combination existing in each cell, and then appling the Dempster＇s Rule of Combination to integrating the all basic probability assignments corresponding to the map patterns existing in the cell into the combined basic probability assignment. Mineral resource potential mapping with the Dempster-Shafer theory is demonstrated on a case study to select mineral resource targets. The experimental results manifest that the model can be compared with the weights of evidence model in the effectiveness of mineral resource target selection.

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.

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.

基于DST和PCR2的自适应信息融合算法

DS理论(DST)在低冲突条件下融合精度高于DSm理论(DSmT),而DSmT在高冲突条件下融合精度高于DST。为进一步提高融合精度,结合DST和DSmT的优点,提出一种基于DST和PCR2的自适应信息融合算法。对DSmT框架下的分配法则进行分析,选择融合精度较高且时间复杂度较低的PCR2分配法则。采用PCR2和DST进行自适应融合,即在冲突率高的情况下使用PCR2,而在冲突率低的情况下使用DST。算例结果表明,该算法能够进一步提高融合结果的准确程度,时间复杂度介于DST和PCR2规则之间。

一种基于决策矩阵的DST-AHP多属性决策方法

针对层次分析法决策时存在两两判断、一致性检验次数过多和判断矩阵残缺性等问题,本文提出了一种基于决策矩阵的DST-AHP多属性决策方法。该方法结合决策矩阵的特征值,构建DST-AHP方法层次结构模型和判断矩阵,并根据判断矩阵定义不同属性下各焦元的基本概率分配函数;然后利用Dempster合成法则对基本概率分配函数值进行合成,依据合成后值对方案进行排序。最后对AHP和DST-AHP两种方法进行比较分析,说明该方法的有效性。

DSmT与DST融合门限改进方法

Dezert-Smarandache理论(DSmT)是一种能够高效实现多源信息融合,成功处理强冲突证据源的数据融合方法,而Dempster-Shafer理论(DST)在证据源冲突低时的融合效果好,运算代价低。将两种技术结合,在冲突距离函数变化率较低时采取DST证据理论,反之采用DSmT融合算法是一种提高信息融合效率的可行方式。研究人员对DSmT和DST二者的单点值转换门限方法已做了探讨,针对单点值门限方法的不足,提出了将冲突距离函数作为判别依据来确定转换门限的方法。该方法有很强的适应性,根据不同的证据组合,能划分是单点值门限还是多点值门限。

基于DST和PCR5的弹道目标识别方法

在反导系统中,基于多传感器多源信息的融合识别,一直是研究的热点。为了解决不精确、不确定、冲突的多源信息的融合问题,结合DST(证据理论)和PCR5(比例冲突再分配准则)在处理不同冲突程度证据时的优势,提出了一种自适应的弹道导弹目标识别方法。首先引入关联系数的概念,用来表征证据间的冲突大小;其次给出了目标综合识别步骤;最后结合2个例子进行了仿真验证。结果表明,该方法识别率高,收敛性快,能够有效解决在识别中不稳定、不一致信息的融合问题,具有一定的应用价值。

基于DST-AHP理论的船舶选型群决策方法研究

针对船舶选定型决策工作中常见的决策信息不完全、指标间相关性与权重确定、决策数据处理合成等问题,提出一种基于层次分析与证据理论的决策方法.该方法按照层次分析法原理确定决策模型结构并计算指标权重,利用分级排序表处理指标相关性,同时按照证据理论原理将决策评价等级转化为同一信度框架内的信度分配,用D-S公式进行合成,最终获得对对象船舶的效用排序.将该方法结论与基于灰色关联理论的船舶选定型决策方法结论进行比较分析,说明其有效性.

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。

Product risk reduction using consensus theory

Product analytics is a blend of computational methods with the express purpose of facilitating the multifaceted process of decision-making based on demographic and consumer preferences. This complex subject is derived from consensus theory and includes structured analytics, categories, and the combination of evidence. The methodology is applicable to a wide range of business, economic, social, political, and strategic decisions. The paper describes a product allocation application to demonstrate the conceots.