Improved Dempster-Shafer Theory Research Articles

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets.

Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion

In this study, two binary versions of the Water Strider Algorithm (WSA) are proposed and applied to optimal feature selection in classification problems. In the new binary versions, the formulations of WSA in continuous space are converted into binary space using group-theoretic operators (in AWSA) and sigmoid function (in BWSA). AWSA, BWSA, genetic algorithm (GA), and binary particle swarm optimization (BPSO) are selected and compared over eighteen well-known datasets from the University of California, Irvine repository. The results of AWSA indicate its satisfactory performance compared to those of other algorithms. Then, they are applied to find optimal features of a structural health monitoring classification problem using two well-known machine learning classifiers, namely k-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms. To further improve the accuracy of the classification models, a decision-level data fusion technique is proposed based on the improved Dempster-Shafer theory. It is demonstrated that the AWSA presents superior results compared to the other algorithms and the suggested decision-level data fusion provides a reliable detection of damage.

A volunteer allocation optimization model in response to major natural disasters based on improved Dempster–Shafer theory

Nowadays, factors such as global climate change, environmental damage, and the impact of human activities have led to an increase in natural disasters, and the frequency of natural disaster problems is increasing, which poses a great threat to the safety of people’s lives and property. The increased frequency of natural disasters has increased the need to focus on the ability to prevent and respond to disasters. The increased frequency of natural disasters has increased the need to focus on the capacity for disaster prevention and mitigation. All parties need to take appropriate measures to reduce losses and provide assistance after a disaster occurs. Among them, volunteers are a newly emerged important force for rescue, but volunteers have characteristics that normal rescue organizations do not have, such as voluntary and fragmented nature, therefore, volunteer allocation becomes an important issue today. This study tries to construct a volunteer assignment method, which takes into account several factors in the assignment, such as volunteers’ own willingness to the disaster site, the competency of volunteers’ various abilities, the demand of the disaster site for the task and the time satisfaction of the disaster victims, etc. L-T2FNs are introduced in the assignment process to enhance the degree of certainty; Prospect theory’s value function to consider the disaster victims’ psychology; This study proposes an algorithm to reduce evidence fusion conflicts. This algorithm uses a differential evolutionary algorithm based on the Dempster–Shafer theory to train the lowest conflict index (determined by K and evidence distance) for BPA fusion. Subsequently, the assignment is demonstrated by using the 7.8 magnitude earthquake in Turkey in 2023 as an arithmetic example to provide a solution to the problem of how to allocate volunteers to the appropriate disaster sites.

A Multi-Source Data Fusion Method for Assessing the Tunnel Collapse Risk Based on the Improved Dempster–Shafer Theory

Collapse is the main engineering disaster in tunnel construction when using the drilling and blasting method, and risk assessment is one of the important means to significantly reduce engineering disasters. Aiming at the problems of random decision-making and misjudgment of single indices in traditional risk assessment, a multi-source data fusion method with high accuracy based on improved Dempster–Shafer evidence theory (D-S model) is proposed in this study, which can realize the accurate assessment of tunnel collapse risk value. The evidence conflict coefficient K is used as the identification index, and the credibility and importance are introduced. The weight coefficient is determined according to whether the conflicting evidence is divided into two situations. The advanced geological forecast data, on-site inspection data and instrument monitoring data are trained by Cloud Model (CM), Gradient Boosting Decision Tree (GBDT) and Support Vector Classification (SVC), respectively, to obtain the initial BPA value. Combined with the weight coefficient, the identified conflict evidence is adjusted, and then the evidence from different sources is fused to obtain the overall collapse risk value. Finally, the accuracy is selected to verify the proposed method. The proposed method has been successfully applied to Wenbishan Tunnel. The results show that the evaluation accuracy of the proposed multi-source information fusion method can reach 88%, which is 16% higher than that of the traditional D-S model and more than 20% higher than that of the single-source information method. The high-precision multi-source data fusion method proposed in this paper has good universality and effectiveness in tunnel collapse risk assessment.

Risk assessment of complex footbridge based on Dempster–Shafer evidence theory using Fuzzy matter–element method

Owing to the landscapes of cities and the requirements of subchannels, the construction of large-span footbridges is complex and extremely risky. These risks may lead to the collapse of a footbridge during construction, resulting in casualties and other catastrophic consequences, delays in projects and significant property losses, and potential safety hazards during follow-up maintenance. At present, China’s urban footbridge construction has not been included in the overall urban construction category, and its risk assessment is static and temporary, mainly relying on the subjective judgment of experts and construction personnel, and the different cases are difficult to learn from each other. To address this issue, a new risk assessment method and a novel system quality management framework are proposed herein. In this system, real-time engineering quality data under 4M1E (Man, Method, Material, Machine, Environment) framework is used to replace traditional risk factors. An improved Dempster–Shafer theory is adopted to effectively combine heterogeneous data from the 4M1E framework and calculate the dynamic total risk index. Finally, the proposed method is verified. The research results show that the new system can predict the risk level of each stage of bridge construction objectively and effectively by using real-time monitoring data, and has high robustness in different project testing.

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.

Structural nonlinear damage detection using improved Dempster-Shafer theory and time domain model

In the service period, a crack may appear in some engineering structures. The development of accurate and effective methods for crack damage detection has become a topic of great importance. In this paper, a nonlinear damage detection method based on the improved Dempster-Shafer (D-S) theory and time domain model is presented. First, acceleration responses in the undamaged and damaged states are measured by using accelerometers. Then, acceleration responses are utilized to establish an autoregressive (AR) model, and residual time series of acceleration responses are used to establish an autoregressive conditional heteroskedasticity (ARCH) model. A cepstral metric conversion (CMC) method based on the AR model is employed to obtain local damage solution and an autoregressive conditional heteroskedasticity conversion (ARCHC) method based on ARCH model is presented to acquire another local damage solution. Finally, the D-S theory is applied to detect damages by integrating these local damage solutions, and an improved D-S theory is further presented to enhance the detection accuracy. The numerical and experimental examples show that the improved D-S theory has high detection accuracy and good performance.

Radar target classification using improved Dempster–Shafer theory

This study considers the problem of coarse classification of targets using multifunction radar. Several methods are available for classification such as decision trees, Dempster–Shafer, Bayes, neural networks, etc. A different approach to assign the mass functions based on fuzzy logic in the Dempster–Shafer framework is proposed in this study. The method is evaluated for classification of different kinds of targets like aircraft, ballistic missiles, satellites, chaff and actual clouds, and unknown targets. With the proposed method, improvement in classification accuracy is observed, compared to existing mass functions. The technique is found to be computationally efficient and suitable for real-time systems.

Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory.

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory.

A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory

Supply chain partner selection (SCPS) is an important issue for firms to remain highly competitive. SCPS is generally formulated as a fuzzy multi-criteria decision-making problem, of which the most crucial step is how to cope with the fuzzy correlations of multi-criteria and individual differences of experts. To gain more exact results, we adopt interval-valued intuitionistic fuzzy sets to express the fuzziness of multi-criteria. Fuzzy Choquet integral model is developed to explain correlations of multi-criteria and calculate the evaluation results of each expert. An improved Dempster–Shafer theory is utilized to reconcile differences and synthesize each expert decision results. Additionally, an example is presented to illustrate the potential application of the proposed method.

Information System Security Risk Evaluation Model Based on Improved Dempster-Shafer Evidence Theory

To deal with the uncertainty of information system security risk evaluation, a risk evaluation model combining Dempster-Shafer evidence theory and grey relational analysis is proposed. In this model, an evaluation index system is established. The risk evaluation rate of information system security is educed by using improved Dempster-Shafer theory. For each risk rating, the Basic Probability Assignment(BPA) function in Dempster-Shafer theory is get by calculating the grey relational grades, so the difficult issue of determining the BPA with Dempster-Shafer theory is solved. The Dempster-Shafer fusion strategy was applied to fuse the evaluation ratings in different data sources for evaluation conclusion. Finally, a case proves that the method is feasible and effective.

Multiple-feature Tracking Based on the Improved Dempster-Shafer Theory

Dempster-Shafer evidence theory is widely used in the fields of decision level information fusion. In order to overcome the problem of the counter-intuitive results encountered when using Dempster’s combination rule to combine the evidences which exist high conflict, a modified sequential weighted evidence combination is proposed. Firstly, the credibility of each evidence can obtained based on K-L distance, besides, the uncertainty of each evidence can obtained based on information entropy. Simultaneously, using the uncertainty of each evidence to improve the credibility of each evidence, then the weights of the bodies of evidence are obtained based on the improved credibility of each evidence, the weights generated are used to modify the bodies of evidence including the previous combination result, the previous evidence and the new arriving body of evidence at current step. Finally, according to the Dempster’s combination rule, the weighted average combination results can be obtained. In the experimental part, the improved method is used to fuse video multiple features in target tracking system and compared the results with the standard D-S theory. The simulation results show that the proposed method has better performance. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3364