A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

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.

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.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

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%.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

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.

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.

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 Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.

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.

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence

In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer(D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.

Network Security Situation Evaluation Based on Modified D-S Evidence Theory

With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.

Analysis of Early-Warning Threshold for Metro Construction Collapse Risk Based on D-S Evidence Theory and Rough Set

The existing early-warning system in metro construction are generally based on traditional single-sensor data and simple analytic model, which makes it difficult to deal with the complex and comprehensive environment in metro construction. In this paper, the framework of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set is built. By combining the primary data fusion collected based on rough set with the secondary data fusion which is based on D-S evidence theory, the integration of multiple information in metro construction is realized and the risk assessment methods are optimized. A case trial based on Hangzhou metro construction collapse accident is also carried out to exemplify the framework. The empirical analysis guarantees the completeness and independence of the prediction information, and realizes the dynamic prediction of the variation trend of metro construction collapse risk.

Assessment of Power Quality Based on D-S Evidence Theory

Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.

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,