Application Of Dempster-Shafer Theory Research Articles

Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory

Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory

Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application.

An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verification are explained. Then the proposed implementation of comparison scores fusion is presented, including details on the application of DST, required modifications, base probability, and mass conversions. Next, the biometric verification process is described, and the engineered biometric banking system principles are provided. Finally, the validation results of three fusion approaches on synthetic and real data are presented and discussed, considering the desired outcome manifested by minimized false non-match rates for various assumed thresholds and biometric verification techniques.

Lithium-ion battery diagnostics and prognostics enhanced with Dempster-Shafer decision fusion

Prognostics is the discipline of predicting the remaining useful life of a component or system in order to optimize the maintenance planning or the mission execution. Prognostics-enabled systems likely reduce the overall life-cycle cost and increase the reliability. In the Bayesian estimation framework, the widely used techniques are the traditional Kalman filter, along with its non-linear extensions, and the particle filter. Each technique has different advantages. This paper investigates the fusion of prognostic results from different techniques in order to achieve a more trustworthy remaining useful life (RUL) prediction, as measured by a reduction in uncertainty. Models for extended Kalman filter (EKF) and particle filter (PF) are developed from the feature data. The results from EKF and PF are then fused using an application of Dempster-Shafer theory (DST). Separate models are utilized for EKF and PF in order to introduce multi-model prognostics and to optimize the performance of each technique for both diagnosis and RUL prediction. Prognostics is triggered when degradation is detected by diagnosis. DST is then applied to the prognostic results from EKF and PF. The result of DST is a density function whose performance can be compared with that of EKF and PF. DST allows for the fusion of multiple sensors and state estimates. The proposed method is verified with the prognosis of a set of lithium-ion batteries.

Prediction of coal and gas outbursts by a novel model based on multisource information fusion

As one type of geologic disaster, coal and gas outbursts seriously threaten safe production in coal mines, restricting the sustainable development of the mining industry. However, coal and gas outbursts are difficult to forecast due to their uncertainty and the limitation of sample size, which affect the accuracy of the traditional prediction methods to some extent. Therefore, this study developed a novel model based on multisource information fusion to realize the predictive progress of coal and gas outburst disasters. Through the application of Dempster-Shafer theory, a method of multisource information fusion, the proposed model combined the results of different forecasting approaches, including conventional techniques and an emerging method based on artificial intelligence. To enhance the performance of the established model, this study improved Dempster-Shafer theory and verified its effectiveness in dealing with highly conflicting information. We then applied this model to the No. 3 coal seam of the Xinjing coal mine, Shanxi Province, China. The fused prediction accurately reflected the situation of outburst hazards and showed good compensation for false prediction. An analysis of the results concluded that the model based on multisource information fusion increases the credibility of the forecast, which might provide technical support for safe coal mine production.

Application of Dempster-Shafer theory in combining the experts’ opinions in DEA

In data envelopment analysis models, values of inputs, outputs and their slack weights are usually based on the domain experts’ opinions. While they play a key role in efficiency evaluation of decision-making units in practice, when there are more than one expert, the manager encounters with the problem of effective specification of final values. The problem may be worsen when the belief degree on the opinions is not complete and differs from one expert to the other, which consequently leads to different and sometimes conflicting analytic results. Belief function defined in Dempster–Shafer theory is a powerful tool to derive a possible solution in these circumstances. We adapt this theory to address such situations in data envelopment analysis. A linear optimisation model is devised as a new combination rule of experts’ opinions, which covers the drawbacks of some existing combination rules in the belief function theory. The methodology is visualised with simple examples. Moreover, the well-known Monte Carlo experimentation is used to test the performance of the proposed method.

Information-based dissimilarity assessment in Dempster–Shafer theory

Dissimilarity assessment is a central problem in the Dempster–Shafer Theory (DST), where the difference in information content between two bodies of evidence (BoEs) should be quantified. Different dissimilarity measures (DMs) have been proposed; however, no single DM seems to be comprehensive enough to compare all aspects of information conveyed by BoEs. The information content of DMs are highly correlated as well. In this paper, DMs are categorized based on their interpretation of information content, emphasizing entropy-like DMs. A methodology is then proposed to select a set of more informative and less overlapping DMs called the “set of most discriminative dissimilarity measures” (smDDM). A forward selection procedure based on an appropriate criterion was utilized and the threshold for selection was derived naturally. To enhance the numerical evaluation, two experimental setups were designed and utilized with the existing setup to provide a sample of dissimilarity values. Comprehensive analysis supports the favorable properties of the proposed smDDM. The selected DMs came naturally from six different categories and subcategories of inner product-based and entropy-like DMs. Optimality analysis shows that the proposed selection procedure resulted in an appropriate near-optimal solution. Dissimilarity assessment is an integrated part of many applications of DST. The applicability and performance of the smDDM was examined and verified for two case studies: evidential clustering and sensor reliability evaluation.

Application of Dempster-Shafer theory for network selection in heterogeneous wireless networks

Network selection in heterogeneous wireless networks aims to choose an optimal network from several candidate wireless networks according to different constraints presented by, for example, applications, subscribers or operators. It has been studied with different methodologies such as decision function (DF) and multi-attribute decision making (MADM). In this paper, the Dempster-Shafer theory (DST) is applied to coping with more complicated scenarios during network selection. The basic idea is to combine the network evaluations (i.e. degrees of belief) judged from the viewpoints of different constraint factors to arrive at an agreement with joint mass for each network. Thus a rank of these networks is obtained and the one with the largest joint mass in the rank will be selected as the best network. The key concepts and the detailed application process of DST for multi-objective decision making of network selection are illustrated. And then two extended applications of DST are discussed for more complicated scenarios during network selection. Simulation results verify the consistency and the effectiveness of the mechanism in contrast to the latest MADM approach.

An approach using Dempster–Shafer theory to fuse spatial data and satellite image derived crown metrics for estimation of forest stand leading species

Leading species at the forest stand level is a required forest inventory attribute. Information regarding leading species enables the calculation of volume and biomass in support of forest monitoring and reporting activities. In this study, approaches for leading species estimation based upon very high spatial resolution (pixel sided <1m) have been developed and implemented, with opportunities for improving attribute accuracy using data fusion methods. Over a study region located in the Yukon Territory, Canada, we apply the Dempster–Shafer Theory (DST) to integrate multiple resolutions of satellite imagery (including spatial and spectral), topographic information, and fire disturbance history records for the estimation of leading species.Among the data source combinations tested in the study, the QuickBird panchromatic combined with selected optical channels from Landsat-5 Thematic Mapper (TM) imagery provided the highest overall accuracy (70.4%) for identifying leading species and improved the accuracy by 3.1% over a baseline from a classification-tree based method applied on all data sources. Additional insights to the application of DST to fuse satellite imagery with ancillary data sources to map leading stand species in a boreal environment are also elaborated upon, including the range and distribution of training data and DST mass function establishment.

A Note on the Development of an Evidence Acceptance Framework, using the Syllogistic Approach

This paper sets out a method for generating an acceptance framework for use in evidence-based proofs by employing arguments aligned with a simple syllogistic principle. The method offers, for large or complex Cases, the potential benefits of simplicity, ease of revision/iteration and ease of checking, particularly if the data is presented in spreadsheet format. Some practical formats are suggested for data presentation. A possible extension of the syllogistic approach into a decision framework is also proposed, based on an application of Dempster-Shafer theory in the assignment and evaluation of belief and plausibility, and thereby allowing an assessment of the validity of evidence.

Applications of Belief Functions in Business Decisions: A Review

In this paper, we review recent applications of the Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension to DST, can provide an alternative solution to a wide range of business problems.