Dempster-Shafer Evidence Research Articles

New Strategies for Boosting Localization Accuracy in Wireless Sensor Nodes

Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. The method evaluates node trust using Dempster-Shafer Evidence Theoryto secure localization against rogue nodes and optimizes the localization process through trilateral and multilateration systems. The SABWP-NL approach demonstrates superior performance in terms of localized nodes and localization error compared to existing techniques like BWP, ROA, and AO. Results show that SABWP-NL achieves the highest number of localized nodes and the lowest localization error, making it a promising solution for efficient and secure node localization in WSNs.

Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory

There exists many large-scale multi-source data, ranging from genetic information to medical records, and military intelligence. The inherent intricacies and uncertainties embedded within these data sources pose significant challenges to the process of information fusion. Owing to its exceptional capacity to represent data uncertainty, Dempster-Shafer (D-S) evidence theory has emerged as a widely utilized approach in information fusion. However, the evidence theory encounters three significant issues when applied to multi-source data information fusion: (1) the conversion of sample information into evidence and the construction of the basic probability assignment (BPA) function; (2) the resolution of conflicting evidence; and (3) the mitigation of exponential explosion in computation. Addressing the aforementioned challenges, this paper delves into the information fusion strategies for large-scale multi-source data based on Dempster-Shafer evidence theory. Initially, the concept of support matrix is introduced and the data matrix is transformed into a support matrix to address the construction challenges associated with BPA. Next, a method for addressing evidence conflicts is introduced by incorporating an additional data source composed of average values. Furthermore, a solution for mitigating high computational complexity is presented through the utilization of a hierarchical fusion approach. Finally, experimental results show that compared with other five advanced information fusion methods, our information method has improved the classification accuracy by 4.66% on average and reduced the time by 66.35% on average. Hence, our method is both efficient and effective, demonstrating exceptional performance in information fusion.

Coal-Mine Water-Hazard Risk Evaluation Based on the Combination of Extension Theory, Game Theory, and Dempster–Shafer Evidence Theory

Due to the complex hydrogeological conditions and water hazards in coal mines, there are multiple indexes, complexities, incompatibilities, and uncertainty issues in the risk evaluation process of coal-mine water hazards. To accurately evaluate the risk of coal-mine water hazards, a comprehensive evaluation method based on extension theory, game theory, and Dempster–Shafer (DS) evidence theory is proposed. Firstly, a hierarchical water-hazard risk-evaluation index system is established, and then matter-element theory in extension theory is used to establish a matter-element model for coal-mine water-hazard risk. The membership relationship between various evaluation indexes and risk grades of coal-mine water-hazard risk is quantified using correlation functions of extension set theory, and the quantitative results are normalized to obtain basic belief assignments (BBAs) of risk grades for each index. Then, the subjective weights of evaluation indexes are calculated using the order relation analysis (G1) method, and the objective weights of evaluation indexes are calculated using the entropy weight (EW) method. The improved combination weighting method of game theory (ICWMGT) is introduced to determine the combination weight of each evaluation index, which is used to correct the BBAs of risk grades for each index. Finally, the fusion of DS evidence theory based on matrix analysis is used to fuse BBAs, and the rating with the highest belief fusion result is taken as the final evaluation result. The evaluation model was applied to the water-hazard risk evaluation of Sangbei Coal Mine, the evaluation result was of II grade water-hazard risk, and it was in line with the actual engineering situation. The evaluation result was compared with the evaluation results of three methods, namely the expert scoring method, the fuzzy comprehensive evaluation method, and the extension method. The scientificity and reliability of the method adopted in this paper were verified through this method. At the same time, based on the evaluation results, in-depth data mining was conducted on the risk indexes of coal-mine water hazards, and it was mainly found that 11 secondary indexes are the focus of coal-mine water-hazard risk prevention and control, among which seven indexes are the primary starting point for coal-mine water-hazard risk prevention and control. The groundwater index in particular has the most prominent impact. These results can provide a theoretical basis and scientific guidance for the specific water-hazard prevention and control work of coal mines.

A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

A multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory

A multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory

Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging

Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon's Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with potential for application in other regions worldwide.

Safety risk assessment of reservoir dam structure: an empirical study in China

Reservoir dam structure is critical to protect public life and property and has always been attention worldwide. However, a systematic approach to assessing the safety risks of reservoir dam structure (RDS) is still required. This study presents a holistic framework for evaluating the safety risk of RDS and develops an evaluation index system. A risk assessment model is constructed based on the cloud and Dempster-Shafer (D-S) evidence theories. The model's validity is confirmed through an empirical analysis of the XY reservoir project. This study offers theoretical insights and practical solutions for managers to facilitate decision-making and supports the advancement of industry standards.

Measures of extended fractional Deng entropy and extropy with applications

Recently, Zhang and Shang introduced modifications to the concept of fractional entropy and proved some properties based on the inverse Mittag-Leffler function (MLF). The Deng entropy serves as a valuable measure in the Dempster-Shafer evidence theory (DST) to tackle uncertainty. In this study, we extend the fractional Deng entropy measure, introducing two distinct versions: E fd 1 α ( m ) and E fd 2 α ( m ) . We call this new measure the extended fractional Deng entropy, EFDEn. Additionally, we apply a similar approach to the fractional Deng extropy measure, resulting in E X fd 1 α ( m ) and E X fd 2 α ( m ) . We call this new measure the extended fractional Deng extropy, EFDEx. These two measures are complementary, leading to provide a deeper analysis of known and unknown information. Subsequently, we conduct a comparative analysis of these measures within the DST framework. We also propose the decomposable fractional Deng entropy, an extension of the decomposable entropy for Dempster–Shafer evidence theory, which effectively decomposes fractional Deng entropy. Finally, we delve into a pattern recognition classification problem to highlight the importance of these new measures.

Research on information fusion of security analysts’ stock recommendations based on two-dimensional D-S evidence theory

Security analysts play a vital role as an information intermediary in the stock market. Their stock recommendations are important references for investors. The efficiency of investment decision-making could be improved by judging the reliability of stock recommendations based on analyst characteristics and fusing the recommendations. We propose an information fusion method for security analysts’ stock recommendations based on two-dimensional Dempster-Shafer (D-S) evidence theory, which comprehensively considers the external and internal characteristics of analysts. The characteristics of analysts are used to measure the reliability of the stock recommendations and modify the evidence, then the D-S fusion rule is used for evidence fusion. Compared with the forecast results of statistical methods and machine learning methods, the two-dimensional D-S evidence theory model we proposed has a higher forecast accuracy, which effectively improves the information efficiency of the stock market and helps investors to make decisions efficiently and scientifically.

An Improved Dempster–Shafer Evidence Theory with Symmetric Compression and Application in Ship Probability

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources.

Combining improved DFMEA with knowledge graph for component risk analysis of complex products

With the development of information technology in manufacturing, product lifecycle iteration is accelerating. Efficiently analyzing and identifying component risks is important to ensure the reliability of products. This paper proposes a new method that combines improved design failure mode and effect analysis (DFMEA) with the knowledge graph for component risk analysis of complex products. A risk evaluation knowledge graph is built for the risk identification and knowledge provision in the process of DFMEA. Graph analysis is used to identify to-be-evaluated objectives based on historical maintenance data, which helps designers avoid the heavy task of identification. The graph matrix of the historical evaluations is used to calculate the comprehensive weights. A Dempster-Shafer (DS) evidence fusion method based on comprehensive weight evaluation information is proposed for the effective fusion of evaluation evidence. As an alternative to the exact risk ranking, we offer a risk trade-off VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method for components classification that classifies components into different risk classes for different risk treatment measures. Finally, the proposed method is validated using a tunnel boring machine as an example, and comparison and sensitivity analyses are performed to demonstrate that the risk value shows a consistent and stable change with expert evaluation.

Hydrogen leakage risk assessment of HECS based on dynamic bayesian network

As a new energy system, the hydrogen-electric coupling system（HECS） involves complex conditions with multiple hazards such as electricity, hydrogen, heat, and high pressure, and there are complex safety risk issues. This paper proposes a hydrogen leakage risk assessment method of HECS based on dynamic Bayesian networks (DBN). Firstly, the bow-tie model is mapped to the DBN model, and the prior probability of the DBN model is obtained using intuitionistic fuzzy theory, Dempster-Shafer evidence theory, and an improved similarity aggregation algorithm. Then, leakage noise or gate models are introduced to improve the conditional probability table of the DBN model, and Weibull distributions are used to construct the probability transfer process between different time periods. Finally, through practical case studies, it is shown that the proposed method can effectively predict the dynamic probability and potential consequences of failure events through time series information. Sensitivity analysis proves the practicality and robustness of the proposed method. The research results can provide theoretical support for risk assessment and daily maintenance of HECS.

Two-stage fusion framework driven by domain knowledge for penetration prediction of laser welding

To solve the problem of industrial application difficulties caused by insufficient depth exploration and poor interpretability of the pure data drive neural network. A two-stage fusion framework driven by domain knowledge was proposed and focused on industrial applications: laser welding penetration monitoring. First, a multi-sensor acquisition system based on plasma and molten pool signals is developed, extracting multiple features from plasma and molten pool images. Two Back Propagation (BP) neural network information fusion models are established in the first fusion stage, leveraging the characteristics of the plasma plume and molten pool front. In the second fusion stage, the Dempster-Shafer (DS) evidence theory is employed to fuse the information from the two BP neural network models. The proposed method, achieving a high accuracy of 98.7 %, is validated by comparing its average accuracy and anti-interference capability with classical Convolutional Neural Network (CNN) and BP neural network models. The physical relationship between input and output is elucidated through an established physical model, enhancing the interpretability and anti-interference capability of the model, and thereby improving the stability and accuracy of the system.

A new distance measure between two basic probability assignments based on penalty coefficient

Uncertainty information processing is a challenging topic in information science. Dempster-Shafer evidence theory shows excellent performance when dealing with uncertainty in information representation, fusion, and processing. The measurement of the distance between the basic probability assignments (BPA) in Dempster-Shafer evidence theory is a powerful tool that deals with uncertainty in evidential information processing. Most of the existing methods are based on the difference in the assessment of evidential degree for different hypotheses, with a notable example Belief-Jensen-Shannon (BJS) divergence. However, these methods ignore the information that is carried by different units in the power sets that represent the hypotheses, i.e., the difference on the hypotheses themselves that include in the evidential degree assessment of different BPA should be considered in the distance measurement. Therefore, in this paper, a novel method grounded in BJS divergence and incorporating a penalty coefficient to rectify the distance between basic probability assignments is proposed. This methodology showcases commendable properties, including symmetry, boundedness, and adherence to the triangle inequality. To demonstrate the efficiency of the proposed distance measurement method between BPAs, we apply it to two common-used machine learning data sets: Iris data set and Dry Beans data set. Compared to the represented existing evidence distance measurement approaches, the proposed method yields superior performance in both data sets with respect to classification accuracy.

In-Situ Quality Intelligent Classification of Additively Manufactured Parts Using a Multi-Sensor Fusion Based Melt Pool Monitoring System

Although laser powder bed fusion (LPBF) technology is considered one of the most promising additive manufacturing techniques, the fabricated parts still suffer from porosity defects, which can severely impact their mechanical performance. Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status; thus, the monitoring accuracy can be improved. However, existing multi-sensing signals are mainly optical and acoustic, and camera-based signals are mostly layer-wise images captured after printing, preventing real-time monitoring. This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors. High-speed cameras, photodiodes, and microphones were used to collect signals during the experimental process. All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images, which enabled the capture of more localized features. Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory, single-sensor, dual-sensor and triple-sensor fusion monitoring models were investigated with the three types of signals, and their performances were compared. The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy, with accuracy rates of 97.98%, 92.63%, and 100% for high-, medium-, and low-quality samples, respectively. Hence, a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process, which has the potential to reduce porosity defects. Finally, the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.

A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making

How to effectively deal with uncertain and imprecise information in decision making is a complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such challenges due to its ability to model uncertainty and imprecision. However, Dempster's rule can sometimes yield counterintuitive results when dealing with highly conflicting evidence. In this paper, we introduce a novel belief sine similarity measure, called $BS^2M$, which effectively measures the discrepancy between different pieces of evidence. We also establish that $BS^2M$ possesses important properties such as boundedness, symmetry, and non-degeneracy. Building upon $BS^2M$, we present a new method for decision making. The proposed method considers both the credibility and the information volume of each evidence, providing a more comprehensive reflection of their importance. To validate our method, we conduct experiment in target recognition application, demonstrating the effectiveness and rationality of the proposed method.

A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory

As coal ore and other resources are continuously mined, a three-zone structure is formed underground consisting of a sagging zone, fault zone, and caving zone. The use of well-logging data to identify the three zones is important for production safety and environmental management. Owing to the scarcity of data that can reflect three zones in normal coal mining, conventional identification and prediction methods face challenges when extracting data features, incurring a degree of uncertainty within prediction results. Accordingly, the accurate identification of the three zones has become a critical objective in daily production. To address this issue, we developed a method called a method called backpropagation neural networks with Dempster–Shafer (DS) evidence theory. Initially, we preprocessed the training data and deployed two backpropagation neural networks (BPNNs) to predict the three zones according to two parameters. According to these prediction results, the local and global credibility of each prediction is calculated and used to obtain the basic probability assignment function required for the DS evidence theory. Finally, the DS evidence theory is used to fuse the two BPNNs prediction results, thereby producing the final prediction results. The proposed method was demonstrated to improve prediction accuracy by 6.4% compared to a conventional neural network.

A quantum group decision model for meteorological disaster emergency response based on D-S evidence theory and Choquet integral

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis.

SAR Image Fusion Classification Based on Improved D-S Evidence Theory

Applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categorization accuracy. However, effective processing of uncertainty information is frequently overlooked by conventional fusion classification systems. This paper presented a fusion classification approach for SAR images based on improved Dempster-Shafer (D-S) evidence theory, taking into account the uncertainty and possible conflicts in the classifiers’ output. First, after adaptively identifying the optimal hyperparameters of models, the SAR data is identified using Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF). Different weights are then allocated in accordance with the variations in classifier performance. Ultimately, enhanced D-S evidence theory is used to integrate the output of these classifiers. In addition, uncertainty in classification results is also modeled and visualized. Experiments with Flevoland and Oberpfaffenhofen datasets show that the accuracy of this method is 85.21% and 91.88% respectively, outperforming both the soft voting ensemble classifier (SVE) and D-S evidence theory fusion method. This approach enhances the comprehension and capacity to manage uncertainty in the model output in addition to increasing the accuracy and reliability of the classification of SAR data.

Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion.

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera-LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster-Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.