Dempster-Shafer Combination Rule Research Articles

Data Interactive Control System Based on Heterogeneous Database of Information Fusion and Internet of Things

Abstract To improve the data interaction control effect of a heterogeneous database and reduce packet loss rate and response time, a new heterogeneous database data interaction control system based on information fusion is designed. The system consists of hardware and software parts. The hardware part of the heterogeneous database data interaction control system is composed of a data extraction module, data sending and receiving module, encryption module, and data interaction control module. On the basis of completing hardware design, the system software is designed, a fuzzy similarity matrix is constructed by calculating the similarity among the evidence, the weight coefficient of each piece of evidence is calculated by calculating the support function of each piece of evidence, the evidence source model is modified by using discount rules, and Dempster–Shafer combination rules are transformed into matrix analysis. Combined with the modified evidence source model, information fusion of the heterogeneous database is carried out to complete the software design of the data interactive control system. The experimental results show that the data interaction control effect of the heterogeneous database in this system is good, and the packet loss rate and response time are low, improving the data interaction control effect.

A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance.

In response to the traditional Dempster-Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (SALSA) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree of conflict between evidences is calculated based on the number of correlation coefficients between evidences. Then, the evidences with a number of correlation coefficients greater than the average number of correlation coefficients of evidence are connected to construct an evidence association network. The authority weight of the evidence is calculated based on the number of citations in the concept of SALSA algorithm combined with the support of the evidence. Subsequently, the Lance-Williams distance between the evidences is calculated and transformed into support of the evidence. Next, the authority weight and support of evidence are combined to jointly construct a novel correction coefficient to correct the evidence. Finally, the corrected evidence is fused using the D-S combination rule to obtain the final fusion result. The numerical results verify that the method proposed in this paper can effectively solve the problem of the traditional D-S combination rule being unable to handle highly conflicting evidence.

An integrated framework for bridge infrastructure resilience analysis against seismic hazard

ABSTRACT Resilient bridge infrastructure is a fundamental component of an uninterrupted transportation system. Thus, assessing the resilience of bridge infrastructure against natural hazards is crucial for transportation agencies. Therefore, the main objective of this study is to develop an integrated framework for analyzing the bridge infrastructure resilience against seismic hazards. The Dempster-Shafer method has been incorporated with the Best Worst Method to achieve this objective and accommodate uncertainty. At first, various resilience criteria have been identified based on an extensive literature review. The weights of the resilience criteria have been determined using the Best Worth Method based on the response provided by the experts. After that, the Dempster-Shafer rule of combination has been used to assess the seismic resilience of a highway bridge by proposing a Bridge Resilience Index. This proposed resilience framework can support transportation agencies in taking effective strategies against seismic hazards.

How to aggregate uncertain and incomplete cognitive evaluation information in lung cancer treatment plan selection? A method based on Dempster-Shafer theory

Multidisciplinary team is beneficial to select an appropriate treatment plan for a patient with lung cancer, where the collected cognitive evaluation information may be uncertain and incomplete. This study dedicates to dealing with a treatment plan selection problem of lung cancer through the multi-criteria analysis with generalised probabilistic linguistic term sets (GPLTSs) which are powerful in describing the uncertainty and incompleteness of subjective evaluations. The existing generalised probabilistic linguistic information aggregation method is based on the Dempster-Shafer combination rule, but the combined results may be counterintuitive. In addition, the GPLTS may not meet the conditions of applying the Dempster-Shafer combination rule. To make up for these gaps, a new combination rule based on Dempster-Shafer theory is introduced. Then, a multi-criteria decision-making (MCDM) process with generalised probabilistic linguistic information based on the proposed combination rule is formed and applied to select the treatment plans of lung cancer associated with a multidisciplinary team. Through the sensitivity analysis and comparative analysis, the advantages of the proposed method are highlighted.

A novel divergence measure of mass function for conflict management

Dempster–Shafer evidence theory, which is an extension of Bayesian probability theory, is a useful approach to realize multisensor data fusion. It uses mass functions to represent uncertainty, which can produce a satisfactory fusion result. However, when the evidence is highly conflicting, using Dempster–Shafer evidence theory fusion rule to combine the evidence will generate the result contrary to common sense. To solve this issue, we propose a new method for conflict management based on Renyi divergence (RD). Then, by combining RD with the mass function, we develop Renyi-Belief divergence (RBD). To expand its utility, we modify it and define the modified Renyi-Belief divergence (MRBD). Our method MRBD integrates the characteristics of mass functions and can handle conflict by measuring the differences between mass functions. Experiments show that MRBD can effectively deal with conflicts. After dealing with the conflicting evidence, we realize multisensor data fusion based on the Dempster–Shafer combination rule. Moreover, we also consider the information quality and belief entropy to reinforce the credibility of evidence. A large number of examples show that the proposed method is feasible and efficient. Finally, in the application of fault diagnosis, our method can effectively determine the fault type.

Conceptual design evaluation considering the ambiguity semantic variables fusion with conflict beliefs: An integrated Dempster-Shafer evidence theory and intuitionistic fuzzy -VIKOR

Quantifying the uncertain linguistic evaluation from decision-makers (DMs) is one of the most challenging parts in the conceptual design decision. Although fuzzy decision models have been widely used to capture potential uncertainty by assigning a fuzzy term with the certain belief, the ambiguity subjective evaluation of semantic variables with conflict beliefs derived from DMs have not been well addressed. To solve this drawback, a concept decision model based on Dempster-Shafer (DS) evidence theory and intuitionistic fuzzy -Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) considering the ambiguity semantic variables fusion is proposed. Firstly, by incorporating semantic variables of intuitionistic fuzzy sets (IFSs), the diversified semantic judgments and its belief will be taken into account to form an ambiguity semantic initial decision matrix; secondly, the DS combination rule will be used to fuse the different semantic variables of multi-DMs in each scheme, update the belief of each semantic variable, and then the semantic fusion value matrix of the scheme will be constructed; finally, the weight of each evaluation objective will be calculated based on the value matrix and information entropy model, IFS-VIKOR model will be constructed to rank the concepts. A case study of the tree climbing and trimming machine will be employed to verify the proposed decision model. This decision model considering diversifying semantic variables and the conflict belief is proven to be effective compared with the IFS-SAW and ISF-TOPSIS.

Enhancing the identification of web genres by combining internal and external structures

Automating the identification of the genre of web pages becomes a promising research area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. Many studies have been proposed to identify the genre of web pages. These studies differ with respect to three main factors which are the features used, the classification algorithm and the list of genres used for the evaluation. The main idea of this paper is to combine the predictions produced by different classifiers using the internal and external structures of a web page. To combine the predictions of the different classifiers we used different OWA operators and the Dempster-Shafer (DS) combination rule. Moreover, we proposed an improved DS combination method based on the ranks of the predicted genres. The experiments conducted using the two known datasets (KI-04 and SANTINIS), show that our study achieves better results in comparison with other ensemble classifiers and genre identification works as well.

Development of flood resilience framework for housing infrastructure system: Integration of best-worst method with evidence theory

Housing infrastructure is the basic need of living, and due to disaster, many houses got damaged. Therefore, assessing resilience for housing infrastructure against a flood hazard is an important task for any community as it gives the real scenario of the capability to resist and recover from the disaster after the occurrence of the hazard. The process of resilience quantification requires a different type of information from different sources, and due to which uncertainty and incomplete data may get involved. There is significantly limited literature available focusing on housing infrastructure resilience; however, the available literature has not incorporated such uncertainty and incomplete information. Therefore, in this work, a resilience assessment framework for housing infrastructure is proposed using a combination approach of Best Worst Method and a Hierarchical Evidential Reasoning based on the Dempster-Shafer theory against flood hazard. The proposed framework is then implemented in Barak valley North-East India to quantify that valley’s resilience and evaluate the model. Initially, different resilience attributes are selected, and based on experts’ opinion, the Best Worst Method rates the criteria to find the weightage. After finding the weightage, flood resilience is evaluated by using the Dempster-Shafer rule of combination. Lastly, sensitivity analysis is also performed to investigate the sensitivity of all the attributes of the proposed hierarchical housing infrastructure resilience model. The proposed flood resilience assessment model generates satisfactory results which indicate the condition state of resilience along with the unassigned degree of belief or uncertainty.

A model to determining the remaining useful life of rotating equipment, based on a new approach to determining state of degradation

Condition assessment is one of the most significant techniques of the equipment’s health management. Also, in PHM methodology cycle, which is a developed form of CBM, condition assessment is the most important step of this cycle. In this paper, the remaining useful life of the equipment is calculated using the combination of sensor information, determination of degradation state and forecasting the proposed health index. The combination of sensor information has been carried out using a new approach to determining the probabilities in the Dempster-Shafer combination rules and fuzzy c-means clustering method. Using the simulation and forecasting of extracted vibration-based health index by autoregressive Markov regime switching (ARMRS) method, final health state is determined and the remaining useful life (RUL) is estimated. In order to evaluate the model, sensor data provided by FEMTO-ST Institute have been used.

Toward a Dempster-Shafer theory of concepts

In this paper, we generalize the basic notions and results of Dempster-Shafer theory from predicates to formal concepts. Results include the representation of conceptual belief functions as inner measures of suitable probability functions, and a Dempster-Shafer rule of combination on belief functions on formal concepts.

Paradox Elimination in Dempster-Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion.

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.

An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure.

Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

A new weighting factor in combining belief function

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences.

PHYSICAL SEISMIC VULNERABILITY ASSESSMENT OF TEHRAN USING THE INTEGRATION OF GRANULAR COMPUTING AND INTERVAL DEMPSTER- SHAFER

Abstract. Several faults exist in the vicinity of Tehran, the capital of Iran such as North Tehran, Ray, Mosha and Kahrizak. One way to assist reducing the damage caused by the earthquake is the production of a seismic vulnerability map. The study area in this research is Tehran, based on the assumption of the activation of North Tehran fault. Degree of Physical seismic vulnerability caused by the earthquake depends on a number of criteria. In this study the intensity of the earthquake, land slope, numbers of buildings’ floors as well as their materials are considered as the effective parameters. Hence, the production of the seismic vulnerability map is a multi criteria issue. In this problem, the main source of uncertainty is related to the experts’ opinions regarding the seismic vulnerability of Tehran statistical units. The main objectives of this study are to exploit opinions of the experts, undertaking interval computation and interval Dempster-Shafer combination rule to reduce the uncertainty in the opinions of the experts and customizing granular computing to extract the rules and to produce Tehran physical seismic vulnerability map with a higher confidence. Among 3174 statistical units of Tehran, 150 units were randomly selected and using interval computation, their physical vulnerabilities were determined by the experts in earthquake-related fields. After the fusion of the experts’ opinions using interval Dempster-Shafer, the information table is prepared as the input to granular computing and then rules are extracted with minimum inconsistency. Finally, the seismic physical vulnerability map of Tehran was produced with % 72 accuracy.

A new weighting factor in combining belief function.

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this problem. First, a modified dissimilarity measurement is proposed which is characterized by both distance and conflict between evidences. Second, a measurement of information volume of each evidence based on Deng entropy is introduced. Then two kinds of weight derived from aforementioned measurement are combined to obtain a new weighting factor and a weighted average method based on the new weighting factor is proposed. Numerical examples are used to illustrate the validity and effectiveness of the proposed method. In the end, the new method is applied to a real-life application of river water quality monitoring, which effectively identify the major land use activities contributing to river pollution.

Evolutionary multi-objective fault diagnosis of power transformers

This paper introduces a two step algorithm for fault diagnosis of power transformers (2-ADOPT) using a binary version of the multi-objective particle swarm optimization (MOPSO) algorithm. Feature subset selection and ensemble classifier selection are implemented to improve the diagnosing accuracy for dissolved gas analysis (DGA) of power transformers. First, the proposed method selects the most effective features in a multi objective framework and the optimum number of features, simultaneously, which are used as inputs to train classifiers in the next step. The input features are composed of DGA performed on the oil of power transformers along with the various ratios of these gases. In the second step, the most accurate and diverse classifiers are selected to create a classifier ensemble. Finally, the outputs of selected classifiers are combined using the Dempster-Shafer combination rule in order to determine the actual faults of power transformers. In addition, the obtained results of the proposed method are compared to three other scenarios: 1) multi-objective ensemble classifier selection without any feature selection step which takes all the features to train classifiers and then applies MOPSO algorithm to find the best ensemble of classifiers, 2) a well-known classifier ensemble technique called random forests, and 3) another powerful decision tree ensemble which is called oblique random forests. The comparison results were favourable to the proposed method and showed the high reliability of this method for power transformers fault classification.

Novel self-interference suppression schemes based on Dempster-Shafer theory with network coding in two-way full-duplex MIMO relay

A two-way full-duplex multiple-input multiple-output (MIMO) relay provides effective spectral efficiency and system capacity improvement. However, self-interference (SI) as a serious problem limits the development of a full-duplex relay. In this paper, the novel SI suppression schemes based on the Dempster-Shafer (DS) evidence theory are proposed to suppress SI in the two-way full-duplex MIMO relay. DS evidence theory can appropriately characterize uncertainty and make full use of multiple evidence source information by DS combination rule to obtain reliable decisions. The first proposed DS network coding (DS-NC) scheme adopts DS evidence theory to detect the signal of each source node, considering SI suppression in the basic probability assignment computation in the multiple access phase. Moreover, different from the DS-NC scheme, the further proposed DS physical-layer network coding (DS-PNC) scheme considers SI suppression from the vector space perspective and combines the PNC mapping rule with the DS theory to obtain a network-coded signal without estimating each source node signal. In the broadcast phase, antenna selection is adopted at the relay and DS evidence theory is also applied at each source node for SI suppression. Meanwhile, the effects of imperfect SI channel estimation and the intended channel estimation on the proposed schemes are also studied. Finally, the proposed DS-PNC scheme is studied with the SI signal model which considers analog radio frequency cancellation, the effects of I/Q modulator imbalances, and power amplifier nonlinearity of the transmitter. Simulation results reveal that the proposed DS-PNC scheme is a little more sensitive to the channel estimation errors than the traditional spatial-domain schemes. However, with the perfect channel state information, the proposed DS-NC with one iteration and DS-PNC schemes outperform traditional spatial-domain schemes. Furthermore, considering the SI signal model with nonlinear distortion and radio frequency cancellation, the DS-PNC scheme is also superior to the traditional schemes. Meanwhile, the DS-PNC scheme is more robust to the SI and achieves the same diversity gain as the ideal cancellation.

Combating Curse of Dimensionality in Resilient Monitoring Systems: Conditions for Lossless Decomposition.

Resilient monitoring systems (RMSs) are sensor networks that degrade gracefully under cyber-attacks on their sensors. The recently developed RMSs, while being effective in the attacked sensors identification and isolation, exhibited a drawback in their operation-an exponentially increasing assessment time as a function of the number of sensors in the network. To combat this curse of dimensionality, a decomposition approach has been proposed, which led to a dramatic reduction of the assessment time, irrespective of the sensor network dimensionality. However, information losses and, thus, reductions in the level of resiliency due to the decomposition, have not been investigated. This paper is intended to carry out such an investigation. Specifically, it derives conditions for lossless decomposition in terms of the Renyi-2 entropy. The development is based on the analysis of matrices, which characterize coupling of process variables and on a monotonicity property of the Dempster-Shafer combination rule on a class of functions, which arise within the RMS operation.

Feature Fusion Using Automatic Generated RBF Neural Network

In this study, a strategy for feature fusion for team behaviors recognition using automatically generated RBF neural network is proposed, for various features need to be extracted in the course of team behaviors recognition and it is difficult to estimate the contribution of various features for identifying and decrypting team behaviors. The burden of high-level recognition algorithm is use eased by using the underlying features of moving target, such as the trajectory characteristics extracted by using the method of trajectory growth. KPCA algorithm is used to nonlinearly reduce dimensionality of extracted characteristics before feature fusion for extracted characteristics of team behaviors. Automatically generated RBF neural network is constructed and feature fusion is realized by using Dempster-Shafer combination rules and network learning. Parameters μ, α and γ are obtained through networks learning in the course of features fusion, s and p is decided by the decreased gradient of output error. The accuracy of behavior recognition is increased dramatically and the processing time is shorten significantly.

Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment

Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves fault identifications, and the fusion diagnosis method based on multi fuzzy evidence matching enhances the precision and reliability of the system fault diagnosis decision furthermore.