Search Results (305)

During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage patterns due to mechanical noise and imaging distance variations. To overcome these physical limitations, this study proposes a physics-informed multimodal fusion framework. Innovatively, we integrate a distance-aware infrared enhancement strategy with vibration spectral subtraction to align heterogeneous data qualities while employing a dual-stream ResNet coupled with Dempster–Shafer (D-S) evidence theory to rigorously resolve inter-modal conflicts at the decision level. Experimental results demonstrate that the proposed strategy achieves a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%). Notably, the fusion mechanism corrects specific misclassifications in micro-cracks and perforation, ensuring a precision exceeding 96.9% across all categories with high stability (standard deviation 0.74%). These findings validate the efficacy of multimodal fusion for precise on-orbit damage assessment, offering a robust solution for spacecraft structural health monitoring. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

As a fundamental mathematical model for complex systems, complex networks describe interactions among social, infrastructural, and biological systems. However, the complete connection structure is often unobservable, making topology reconstruction from limited data—such as time series of unit states—a crucial challenge. To address network reconstruction under sparse local observations, this paper proposes a novel framework that integrates epidemic dynamics with Dempster–Shafer (DS) evidence theory. The core of our method lies in a two-level belief fusion process: (1) Intra-node fusion, which aggregates multiple independent SIR simulation results from a single seed node to generate robust local evidence represented as Basic Probability Assignments (BPAs), effectively quantifying uncertainty; (2) Inter-node fusion, which orthogonally combines BPAs from multiple seed nodes using DS theory to synthesize a globally consistent network topology. This dual-fusion design enables the framework to handle uncertainty and conflict inherent in sparse, stochastic observations. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach. It achieves stable and high reconstruction accuracy on both a synthetic 16-node benchmark network and the real-world Zachary’s Karate Club network. Furthermore, the method scales successfully to four large-scale real-world networks, attaining an average accuracy of 0.85, thereby confirming its practical applicability across networks of different scales and densities. Full article

The growth of the cruise industry and rising passenger numbers have led to an increase in cruise-related accidents, presenting challenges for mass rescue operations. It is crucial to understand the evolution of MAss Life-Threatening Incidents at Sea (MALTISs) in order to make effective decisions in such situations. This study, therefore, presents a scenario deduction model for MALTIS, integrating knowledge element theory, Bayesian Networks (BNs), fuzzy set theory, and improved Dempster–Shafer (DS) evidence theory. Based on knowledge element theory, this study identifies the scenario elements in typical maritime accidents. Given the large scale and complex disaster chain characteristics of MALTISs, the BN method is employed to convert the scenario elements into BN nodes, therefore constructing the MALTIS deduction model. To minimize the subjectivity associated with expert assessments, this study combines fuzzy set theory and the improved DS evidence theory to integrate the opinions of multiple experts, thereby enhancing the reliability of the model’s deduction. BN inference is then used to calculate the probabilities of various situational states, and sensitivity analysis is conducted to identify the key nodes. The Costa Concordia grounding incident serves as an empirical case study. The deduction results closely align with the actual accident evolution, and sensitivity analysis reveals five critical nodes in the event’s progression. This validates the effectiveness of the proposed scenario deduction model. These findings demonstrate that the model can effectively support emergency decision-making in MALTISs. Full article

Dempster’s combination rule in Dempster–Shafer theory is a powerful and effective tool for multi-sensor data fusion. However, counterintuitive results are possible under the condition of a high conflict between pieces of evidence. This study demonstrates that existing conflict measurements cannot prevent such results and, thus, proposes a quantitative conflict measurement based on the concept of essential conflict. This work analyzes two characteristics of the essential conflict, namely belief absolutization and uncorrectable assertions. In addition, considering the desirable properties of the measurement, this study demonstrates that the measurement of essential conflict can reveal the essence of counterintuitive results in Dempster’s combination process. Finally, properties and examples are used to validate the proposed measurement. Full article

This study proposes a collision-risk assessment framework that combines an interval type-2 fuzzy inference system with Dempster–Shafer evidence theory to more reliably evaluate vessel collision risk under the uncertainty inherent in AIS-based marine navigation data. The fuzzy system models membership-function uncertainty through a footprint of uncertainty and produces time-indexed basic probability assignments that are subsequently combined through a Dempster–Shafer–based temporal integration process. Robust combination rules are incorporated to mitigate the counterintuitive results often produced by classical evidence combination. Furthermore, Lenart’s time-based criterion and Fujii’s spatial safety domain are unified to construct a three-level risk labeling scheme, overcoming the limitations of conventional binary risk classification. Case studies using real AIS data demonstrate improved predictive accuracy and significantly reduced uncertainty, particularly when using the robust symmetric combination rule. Overall, the proposed framework provides a systematic approach for handling structural uncertainty in maritime environments and supports more reliable collision-risk prediction and safer navigational decision-making. Full article

The Dempster–Shafer (D–S) evidence theory, while powerful for uncertainty reasoning, suffers from mathematical limitations in high–conflict scenarios where its combination rule produces counterintuitive results. This paper introduces a reformulated D–S framework grounded in optimization theory and information geometry. We rigorously construct a dynamic weight allocation mechanism derived from minimizing systemic Jensen–Shannon divergence and propose a conflict–adaptive fusion rule with theoretical guarantees. We formally prove that our framework possesses the Conflict Attenuation Property and Robustness to Outlier Evidence. Extensive Monte Carlo simulations in multi–UAV explosion point localization demonstrate the framework’s superiority, reducing localization error by 75.6% in high–conflict scenarios compared to classical D–S. This work provides not only a robust application solution but also a theoretically sound and generalizable mathematical framework for multi–source data fusion under uncertainty. Full article

Addressing the technical challenges in detecting Low-Slow-Small Unmanned Aerial Vehicle (LSS-UAV) cluster targets, such as weak signals and complex environmental interference coupling with strong features, this paper proposes a visible-infrared multi-modal fusion detection method based on deep learning. The method utilizes deep learning techniques to separately identify morphological features in visible light images and thermal radiation features in infrared images. A hierarchical multi-modal fusion framework integrating feature-level and decision-level fusion is designed, incorporating an Environment-Aware Dynamic Weighting (EADW) mechanism and Dempster-Shafer evidence theory (D-S evidence theory). This framework effectively leverages the complementary advantages of feature-level and decision-level fusion. This effectively enhances the detection and recognition capability, as well as the system robustness, for LSS-UAV cluster targets in complex environments. Experimental results demonstrate that the proposed method achieves a detection accuracy of 93.5% for LSS-UAV clusters in complex urban environments, representing an average improvement of 18.7% compared to single-modal methods, while the false alarm rate is reduced to 4.2%. Furthermore, the method demonstrates strong environmental adaptability, maintaining high performance under challenging conditions such as nighttime and haze. This method provides an efficient and reliable technical solution for LSS-UAV cluster target detection. Full article

Cognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge. Hence, a novel “Deep Wavelet Cyclostationary Independent Gaussian Markov Fourier Transform Dempster–Shafer Network” is proposed. When the signal waveform is submerged within the noise envelope and residual correlation emerges in the noise, it violates white Gaussian assumptions, leading to misidentification of signal presence. To resolve this, the Adaptive Continuous Wavelet Cyclostationary Denoising Autoencoder (ACWC-DAE) is introduced, in which the Adaptive Continuous Wavelet Transform (ACWT), Cyclostationary Independent Component Analysis Detection (CICAD), and Denoising Autoencoder (DAE) are introduced into the first hidden layer of a Deep Q-Network (DQN). It restores the bursty signal structure, separates the structured noise, and reconstructs clean signals, leading to accurate signal detection. Additionally, bursty and fading-affected primary user signals become fragmented and dip below the noise floor, causing conventional fixed-window sensing to fail in accumulating reliable evidence for detection under intermittent and low-duty-cycle conditions. Therefore, the Adaptive Gaussian Short-Time Fourier Transform Dempster–Shafer Model (AGSTFT-DSM) is incorporated into the second DQN layer, Adaptive Gaussian Mixture Hidden Markov Modeling (AGMHMM) tracks the hidden activity states, Adaptive Short-Time Fourier Transform (ASFT) resolves brief signal bursts, and Dempster–Shafer Theory (DST) fuses uncertain evidence to infer occupancy, thereby detecting an accurate user signal. The results obtained by the proposed model have a low error and detection time of 0.12 and 30.10 ms and a high accuracy of 97.8%, revealing the novel insight that adaptive wavelet denoising, along with uncertainty-aware evidence fusion, supports reliable spectrum detection under low-SNR conditions where existing models fail. Full article

The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster–Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks. Full article

Inter-basin water transfer projects (IBWTPs) play a crucial role in addressing the uneven spatial and temporal distribution of water resources and ensuring water security in the receiving areas. However, these projects are subject to various risk factors during their operation. While risk management is critical, current research in this field lacks a systematic and dynamic approach. A three-dimensional measurement model for probability, loss, and risk value, based on Dempster–Shafer (DS) evidence theory, Bayesian networks, and the equivalence method, was established in this study and, in consideration of the engineering characteristics of the IBWTP, a dynamic transmission evolution model for risk is constructed. The applicability and effectiveness of the model are demonstrated through a case study of the Central Line Project of South-to-North Water Diversion (CLPSNWD). The results indicate that the system risk of the CLPSNWD is in an unstable state, with the key influencing factors being channel engineering risk, flood disaster risk, pipeline engineering risk, and water transfer (discharge) cross-structure risk. The research findings offer a novel approach to the quantitative analysis and evolution of risk and contribute to the further development of engineering risk management theory. Full article

In autonomous driving and mobile robotics applications, constructing accurate and reliable three-dimensional semantic maps poses significant challenges in resolving conflicts and uncertainties among multi-frame observations in complex environments. Traditional deterministic fusion methods struggle to effectively quantify and process uncertainties in observations, while existing evidential deep learning approaches, despite providing uncertainty modeling frameworks, still exhibit notable limitations when dealing with spatially varying observation quality. This paper proposes a selective learnable discounting method for deep evidential semantic mapping that introduces a lightweight selective $\alpha$-Net network based on the EvSemMap framework proposed by Kim and Seo. The network can adaptively detect noisy regions and predict pixel-level discounting coefficients based on input image features. Unlike traditional global discounting strategies, this work employs a theoretically principled scaling discounting formula, ${\widehat{e}}_k\left(x\right)=\alpha \left(x\right)·e_k\left(x\right)$, that conforms to Dempster–Shafer theory, implementing a selective adjustment mechanism that reduces evidence reliability only in noisy regions while preserving original evidence strength in clean regions. Theoretical proofs verify three core properties of the proposed method: evidence discounting under preservation (ensuring no loss of classification accuracy), valid uncertainty redistribution validity (effectively suppressing overconfidence in noisy regions), and optimality of discount coefficients (achieving the matching of the theoretical optimal solution of ${\alpha }^{*}\left(x\right)=1-N\left(X\right)$). Experimental results demonstrate that the method achieves a 43.1% improvement in Expected Calibration Error (ECE) for noisy regions and a 75.4% improvement overall, with $\alpha$-Net attaining an IoU of 1.0 with noise masks on the constructed synthetic dataset—which includes common real-scenario noise types (e.g., motion blur, abnormal illumination, and sensor noise) and where RGB features correlate with observation quality—thereby fully realizing the selective discounting design objective. Combined with additional optimization via temperature calibration techniques, this method provides an effective uncertainty management solution for deep evidential semantic mapping in complex scenarios. Full article

To address the limitations of single-sensor perception in inland vessel monitoring and the lack of robustness of traditional tracking methods in occlusion and maneuvering scenarios, this paper proposes a hierarchical multi-target tracking framework that fuses Light Detection and Ranging (LiDAR) data with Automatic Identification System (AIS) information. First, an improved adaptive LiDAR tracking algorithm is introduced: stable trajectory tracking and state estimation are achieved through hybrid cost association and an Adaptive Kalman Filter (AKF). Experimental results demonstrate that the LiDAR module achieves a Multi-Object Tracking Accuracy (MOTA) of 89.03%, an Identity F1 Score (IDF1) of 89.80%, and an Identity Switch count (IDSW) as low as 5.1, demonstrating competitive performance compared with representative non-deep-learning-based approaches. Furthermore, by incorporating a fusion mechanism based on improved Dempster–Shafer (D-S) evidence theory and Covariance Intersection (CI), the system achieves further improvements in MOTA (90.33%) and IDF1 (90.82%), while the root mean square error (RMSE) of vessel size estimation decreases from 3.41 m to 1.97 m. Finally, the system outputs structured three-level tracks: AIS early-warning tracks, LiDAR-confirmed tracks, and LiDAR-AIS fused tracks. This hierarchical design not only enables beyond-visual-range (BVR) early warning but also enhances perception coverage and estimation accuracy. Full article

This article examines the role of fairness in software across diverse application contexts, with a particular emphasis on healthcare, and introduces the concept of algorithmic (individual) meta-fairness. We argue that attaining a high degree of fairness—under any interpretation of its meaning—necessitates higher-level consideration. We analyze the factors that may guide the choice of a fairness definition or bias metric depending on the context, and we propose a framework that additionally highlights quality criteria such as accountability, accuracy, and explainability, as these play a crucial role from the perspective of individual fairness. A detailed analysis of requirements and applications in healthcare forms the basis for the development of this framework. The framework is illustrated through two examples: (i) a specific application to a predictive model for reliable lower bounds of BRCA1/2 mutation probabilities using Dempster–Shafer theory, and (ii) a more conceptual application to digital, feature-oriented healthcare twins, with the focus on bias in communication and collaboration. Throughout the article, we present a curated selection of the relevant literature at the intersection of ethics, medicine, and modern digital society. Full article

This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the rolling bearing are used as dual-channel data sources to extract multi-dimensional features from time and frequency domains. Then, cloud models are employed to build models for each feature under different conditions, utilizing three digital characteristic parameters to characterize the distribution and uncertainty of features under different operating conditions. Thus, the membership degree vectors of test samples from two channels can be calculated using reference models. Subsequently, D-S evidence theory is applied to fuse membership degree vectors of the two channels, effectively enhancing the robustness and accuracy of the diagnosis. Experiments are conducted on the rolling bearing fault dataset from Case Western Reserve University. Results demonstrate that the proposed method achieves an accuracy of 96.32% using evidence fusion of the drive-end and fan-end data, which is obviously higher than that seen in preliminary single-channel diagnosis. Meanwhile, the final results can give suggestions of the possibilities of anther, which is benefit for technicists seeking to investigate the actual situation. Full article