Search Results (95)

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

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

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

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 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 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

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

Pilot visual behavior safety assessment is a cross-disciplinary technology that analyzes pilots’ gaze behavior and neurocognitive responses. This paper proposes a multimodal analysis method for pilot visual behavior safety, specifically for cognitive state decoding. This method aims to achieve a quantitative and efficient assessment of pilots’ observational behavior. Addressing the subjective limitations of traditional methods, this paper proposes an observational behavior detection model that integrates facial images to achieve dynamic and quantitative analysis of observational behavior. It addresses the “Midas contact” problem of observational behavior by constructing a cognitive analysis method using multimodal signals. We propose a bidirectional long short-term memory (LSTM) network that matches physiological signal rhythmic features to address the problem of isolated features in multidimensional signals. This method captures the dynamic correlations between multiple physiological behaviors, such as prefrontal theta and chest-abdominal coordination, to decode the cognitive state of pilots’ observational behavior. Finally, the paper uses a decision-level fusion method based on an improved Dempster–Shafer (DS) evidence theory to provide a quantifiable detection strategy for aviation safety standards. This dual-dimensional quantitative assessment system of “visual behavior–neurophysiological cognition” reveals the dynamic correlations between visual behavior and cognitive state among pilots of varying experience. This method can provide a new paradigm for pilot neuroergonomics training and early warning of vestibular-visual integration disorders. Full article

To address the difficulty frontline commanders face in accurately perceiving fireground risks during the early stages of oil storage tank fires, in this study, we propose a method that integrates complex network theory with a multi-source information fusion approach based on cloud models and Dempster-Shafer (D-S) evidence theory for situational analysis and dynamic perception. Initially, the internal evolution of accident scenarios within individual tanks is modeled as a single-layer network, while scenario propagation between tanks is represented through inter-layer connections, forming a multi-layer complex network for the storage area. The importance of each node is evaluated to assess the risk level of scenario nodes, enabling preliminary situational awareness, with limited reconnaissance information. Subsequently, the cloud model’s capability to handle fuzziness is combined with D-S theory’s strength in fusing multi-source data. Multi-source heterogeneous information is integrated to obtain the confidence levels of key nodes across low, medium, and high-risk categories. Based on these results, high-risk scenarios in oil storage tank emergency response are dynamically adjusted, enabling the updating and prediction of accident evolution. Finally, the proposed method is validated using the 2015 Gulei PX plant explosion case study. The results demonstrate that the approach effectively identifies high-risk scenarios, enhances dynamic situational perception, and is generally consistent with actual accident progression, thereby improving emergency response capability. Full article

Low-Altitude and Slow-Speed Small (LSS) targets pose significant challenges to air defense systems due to their low detectability and complex maneuverability. To enhance defense capabilities against low-altitude targets and assist in formulating interception decisions, this study proposes a new threat assessment algorithm based on multisource data fusion under visible-light detection conditions. Firstly, threat assessment indicators and their membership functions are defined to characterize LSS targets, and a comprehensive evaluation system is established. To reduce the impact of uncertainties in weight allocation on the threat assessment results, a combined weighting method based on bias coefficients is proposed. The proposed weighting method integrates the analytic hierarchy process (AHP), entropy weighting, and CRITIC methods to optimize the fusion of subjective and objective weights. Subsequently, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Dempster–Shafer (D-S) evidence theory are used to calculate and rank the target threat levels so as to reduce conflicts and uncertainties from heterogeneous data sources. Finally, the effectiveness and reliability of the two methods are verified through simulation experiments and measured data. The experimental results show that the TOPSIS method can significantly discriminate threat values, making it suitable for environments requiring rapid distinction between high- and low-threat targets. The D-S evidence theory, on the other hand, has strong anti-interference capability, making it suitable for environments requiring a balance between subjective and objective uncertainties. Both methods can improve the reliability of threat assessment in complex environments, providing valuable support for air defense command and control systems. Full article

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies. Full article

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article