Search Results (8)

The integration of complementary sensing modalities provides a basis for accurate crack identification in advanced aircraft structures. In this context, PZT transducers are sensitive to incipient damage through guided-wave interrogation, whereas strain gauges capture quasi-static deformation. Prevailing fusion paradigms, however, encounter an interpretability–adaptability dilemma. Model-based approaches lack robustness to sensor degradation, while data-driven attention methods sacrifice transparency. To resolve this trade-off, an Evidence-guided Attention Neural Network (EANN) is proposed. Its central methodological contribution lies in repositioning Dempster–Shafer (D-S) evidence theory from a terminal fusion operator to an upstream credibility feature extraction module. Evidence-derived credibility features, comprising belief entropy, inter-source similarity, and Kalman-filtered residuals, drive the attention weight optimization and endow the learned channel weights with physically interpretable evidential meaning. Ablation experiments confirm that the observed improvement arises from the interaction between the evidence-guided credibility representation and adaptive attention weighting, with neither component sufficient on its own. The framework fuses quasi-static strain measurements with active piezoelectric guided-wave interrogation, which offers high sensitivity to incipient damage but remains vulnerable to channel degradation. Experiments on aluminum tensile plates and trapezoidal wing skin specimens show that EANN maintains identification accuracy under simulated sensor anomalies and partial failures by attenuating compromised channels without explicit fault detection, providing an uncertainty-aware fusion framework for online structural health monitoring of aerospace structures. Full article

Multi-source information fusion is a key challenge in uncertainty reasoning. Dempster–Shafer evidence theory (D-S evidence theory) offers a flexible framework for representing and fusing uncertain information. However, the classical Dempster’s combination rules may yield counter-intuitive results when faced with highly conflicting evidence. To overcome this limitation, we introduce a cluster-level information fusion framework, which shifts the focus from pairwise evidence comparisons to a more holistic cluster-based perspective. A key contribution is a novel cluster–cluster divergence measure that jointly captures the strength of belief assignments and structural differences between clusters. Guided by this measure, a reward-driven evidence assignment rule dynamically allocates new evidence to enhance inter-cluster separability while preserving intra-cluster coherence. Building upon the resulting structure, we propose a two-stage information fusion algorithm that assigns credibility weights at the cluster level. The effectiveness of the framework is validated through a range of benchmark pattern classification tasks, in which the proposed method not only improves classification accuracy compared with D-S evidence theory methods but also provides a more interpretable, cluster-oriented perspective for handling evidential conflict. Full article

Miscarriages of justice led to concerns that forensic science reports were prosecution-biassed and led to elementary errors of probability. The European Network of Forensic Science Institutes (EFNSI) and other institutes developed standards requiring reporting of the probability of evidence under all hypotheses (usually prosecution and defence hypotheses) with the likelihood ratio (LR). $LR={\displaystyle \frac{p\left(E|H_p\right)}{p\left(E|H_d\right)}},$ values > 1, being probative for a prosecution hypothesis. In elementary two-variable conditional probability theory (Baye’s theorem), the LR is also an updating factor which multiplies the odds of guilt for each item of evidence considered. Although this is not true for multiple-variable probability theory, the value of the LR as a valid measure of evidential probity remains. Forensic scientists are experts in evidence and should not stray into the role of the Court to consider the probability of the hypotheses given the totality of the evidence: $p\left(H_p{,H}_d|,E_1,E_2\dots E_n\right).$ Medical experts may be required to assist the court with diagnoses (the hypothesis), but this privilege is balanced by vigilance that experts do not stray beyond their expertise. A narrow interpretation of expertise hinders the evaluation of the evidence under hypotheses adjacent to the area of expertise. This paradox may be overcome by experts declaring competence in areas adjacent to their main area of expertise. Regulatory bodies do not currently require medical experts to adhere to EFNSI guidelines in evaluative reporting. Legal opinion is divided on whether probability theory can be applied to cases requiring medical expertise. Medical experts should, in their reports, clearly separate evaluating the probability of the evidence (where evaluative reporting should apply) and evaluating the probability of hypotheses where methodology should be prioritised over opinion. The reckless misapplication of elementary probability theory, typically transposing conditional probabilities or neglecting prior odds, may lead to the jury being misled into believing posterior odds of guilt are many orders of magnitude greater than reality. Medical experts should declare training in elementary probability theory. Inaccurate probabilities are a joint enterprise between all who inform or advise the jury, so all must be trained in elementary probability theory. Full article

Stormwater pipe infrastructure is a fundamental requirement of any nation, but pipes can be damaged in natural disasters. Consequently, evaluating the resilience of stormwater infrastructure to earthquake damage is an essential duty for any city because it outlines the capability to recover from a disaster after the event. The resilience quantification process needs various data types from various sources, and uncertainty and partial data may be included. This study recommends a resilience assessment framework for stormwater pipe infrastructure facing earthquake hazards using Hierarchical Evidential Reasoning (HER) on the basis of the Dempster–Shafer (D-S) theory. The developed framework was implemented in the City of Regina, SK, Canada to quantify the resilience of the stormwater pipe infrastructure. First, various resilience factors were identified from the literature. Based on experts’ judgment, the weight of these factors was determined using the Best Worst Method (BWM). After that, the resilience was determined using the D–S theory. Finally, sensitivity analysis was conducted to examine the sensitivity of the factors of the recommended hierarchical stormwater infrastructure resilience model. The recommended earthquake resilience assessment model produced satisfying outcomes, which showed the condition state of resilience with the degree of uncertainty. Full article

Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification. Full article

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either $dynamic$ or $static$ depending on their movement characteristics. A LiDAR point cloud is also segmented into $dynamic$ and $static$ points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into $dynamic$ and $static$ points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: $dynamic$ , $static$ , and $unknown$ . Points are placed in the $unknown$ class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods. Full article

Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster–Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user’s and producer’s accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification. Full article

There is continuously increasing interest in research on multi-sensor data fusion technology. Because Dempster’s rule of combination can be problematic when dealing with conflicting data, there are numerous issues that make data fusion a challenging task, including the exponential explosion, Zadeh Paradox, and one-vote veto. These issues lead to a great difference between the fusion results and real results. This paper applies the idea of analyzing distance-based evidence conflicts, introduces the concept of vector space, and proposes a new cosine theorem-based method of identifying and expressing conflicting data. In addition, this paper proposes a new data fusion algorithm based on the degree of mutual support between beliefs, which is based on the Jousselme distance-based combination rule proposed by Deng et al. Simulation results demonstrate that the presented algorithm achieves great improvements in both the accuracy of identifying conflicting data and that of fusing conflicting data. Full article