Search Results (4,067)

This paper presents an absolute distance measurement system based on three-wavelength synchronous phase-shifting interferometry. A synthetic wavelength chain is established using three semiconductor lasers in an all-fiber Fizeau interferometer. By integrating a piezoelectric transducer (PZT)-driven sinusoidal phase modulation with multi-channel synchronous sampling for phase demodulation, and further combining it with a fractional multiplication method, the proposed system achieves high-precision absolute distance measurement over an extended range. Experimental results demonstrate an unambiguous measurement range of 240 μm, a static measurement precision better than 0.6 nm, and a dynamic displacement measurement accuracy superior to 2 nm in comparison with the reference device. The main error sources of the system, including synthetic wavelength uncertainty, phase measurement uncertainty, and air refractive index uncertainty, are systematically modeled and analyzed. In addition, the influence of dynamic factors, such as PZT nonlinearity, is discussed and compensated. The proposed method provides a robust and high-precision solution for absolute ranging and shows strong potential for applications in industrial precision inspection and optical sensing. Full article

Quadrotor motion planning in cluttered environments presents significant challenges in achieving both computational efficiency and trajectory smoothness, particularly in low-altitude economy and intelligent energy system applications where autonomous aerial vehicles perform infrastructure inspection and power line monitoring. Many existing methods either rely on sampling-based algorithms that suffer from long computation times and suboptimal paths, or employ trajectory representations that produce high-order derivative discontinuities unsuitable for agile flight. In this work, we propose an efficient hierarchical motion planning framework that integrates a JPS–Bresenham-based path search with safe flight corridor construction and Bézier curve optimization. Our approach addresses trajectory generation through a two-stage process: a front-end path search that efficiently identifies collision-free paths with reduced waypoints, followed by a back-end optimization that leverages convex safe corridors with overlapping regions to expand the solution space. Through comprehensive benchmark experiments across six different map scenarios, we demonstrate that our method outperforms RRT* and PRM in both path quality and computational efficiency. Monte Carlo experiments across varying map sizes and obstacle densities confirm robustness and scalability advantages. Comparative studies with state-of-the-art planners demonstrate superior success rates and cost efficiency while maintaining strict kinodynamic feasibility. The Bézier-based optimization reduces snap integral by up to 55% compared to ordinary polynomial approaches, demonstrating its superiority for fast quadrotor trajectory planning in complex environments. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

To address the challenges of multi-scale defect perception and complex background interference in railway insulator detection, this paper proposes LSD-YOLO, a lightweight multi-scale fusion network based on an improved YOLO11n. The model integrates three core modules: a Large-Small (LS) module for multi-scale receptive field fusion, a Token Statistics Self-Attention (TSSA) module for efficient global context modeling, and a Detail-Preserving Contextual Fusion (DPCF) module for adaptive multi-scale feature fusion. Experiments on a multi-defect insulator dataset constructed from 4C inspection system images and public datasets show LSD-YOLO achieves 86.2% mAP@50, 4.1 percentage points higher than the baseline model. Its precision and recall reach 91.8% and 80.6% respectively, with only 2.30 M parameters. Its comprehensive detection performance outperforms mainstream comparative models. The proposed method enhances the integrated detection ability for both physical defects and pollution-flashover faults of insulators, and provides a reference for intelligent inspection in complex railway scenarios. Full article

Industrial surface inspection is fundamental to advanced manufacturing, yet reliable robotic image acquisition in complex geometries remains challenging due to severe occlusions and the inherent trade-off between resolution and coverage. Inspired by human visual inspection behaviors and perception–action coordination mechanisms, this paper proposes a hierarchical trajectory optimization framework for robotic image acquisition based on measured point clouds. Specifically, a multi-constraint preprocessing model is developed to emulate human-like active perception strategies, enabling occlusion-aware viewpoint generation over complex concave and convex surfaces with adaptive camera orientation. Building upon this, a multi-objective trajectory optimization method is introduced to coordinate global coverage and local motion efficiency, jointly optimizing viewpoint sequencing, path length, and motion smoothness hierarchically. To further enhance flexibility in constrained environments, a Pose Reachability Augmented Generative Adversarial Network (PRAGAN) is proposed to learn feasible and adaptable imaging postures under kinematic constraints. Experimental results on an industrial robotic platform equipped with 2D and 3D vision systems demonstrate 100% coverage of key surface areas, a 47.0% reduction in path length, and a 37.5% decrease in solution time compared with the baseline in the physical experiments, while ensuring collision-free operation. Both simulation and real-world experiments validate that the proposed framework effectively captures human-inspired perception and motion coordination, providing a practical and scalable solution for complex industrial surface inspection. Full article

Planetary gear systems offer compact design and high-power density, but they are strongly influenced by transmission error (TE), which originates from geometric deviations and elastic deflections. This study presents a dynamic model that integrates elastic compliance, mesh stiffness, damping, and error excitation to evaluate coupled gear responses. Numerical results show that planet–ring contacts undergo larger forces and deflections than sun–planet meshes. Time–frequency analysis with continuous wavelet transform (CWT) reveals nonstationary vibration patterns, while gear tooth flank inspection confirms torque bias and micro-pitting. The findings connect modeling predictions with observed wear, offering insights for planetary gear diagnostics and design. Full article

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article

Electricity theft is a critical source of non-technical losses in modern power systems, causing substantial financial and operational challenges for utilities. Traditional detection methods, such as manual inspections, are inadequate to detect advanced theft techniques, including meter tampering and cyberattacks on smart grids. This study introduces a machine learning-based framework for electricity theft detection using the TDD2022 dataset (derived from OEDI) and evaluates multiple algorithms—Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost, Extra Trees, and Logistic Regression. To address class imbalance, SMOTE is applied, while feature selection leverages LASSO and ReliefF. Experiments compare electricity-only data with multi-utility inputs (electricity and gas) under balanced and imbalanced conditions. Results show that tree-based ensembles, particularly Extra Trees combined with SMOTE and ReliefF, achieve superior performance (accuracy $>95\%$, AUC $\approx 0.99$). Consumer-specific models outperform global models, with commercial classes yielding near-perfect detection, while residential profiles remain challenging. The findings highlight the importance of tailored modeling and feature selection for scalable, accurate theft detection in smart grid environments. Full article

Background: Informal Street food vending plays a vital role in urban food systems by supporting livelihoods and improving access to affordable meals. Despite this contribution, persistent food hygiene and safety challenges continue to threaten public health and business sustainability. Existing research largely frames hygiene as a regulatory compliance issue, with limited empirical attention to how hygiene practices are associated with enterprise performance. Guided by the Health Belief Model (HBM) and the Balanced Scorecard (BSC), this study examined the relationship between food hygiene and safety practices, behavioural compliance, and business sustainability among informal food vendors. Methods: A cross-sectional mixed-methods design was used, combining vendor interviews (n = 30) and structured stall observations (n = 30). Quantitative data were analysed using descriptive and inferential statistics. Qualitative data were thematically analysed. Results: Only 50% of vendors held a valid Certificate of Acceptability (COA), despite 83% reporting prior inspections. Vendors operating for over seven years were significantly more likely to be certified (χ² = 8.23, p = 0.005), and certification was strongly associated with regulatory awareness (χ² = 16.12, p < 0.001). Although 70% reported awareness and 77% prior hygiene training, gaps persisted in sanitation, pest control, and consistent hygiene practices. Compliance was significantly associated with gender and education level (p < 0.05), as well as business duration and inspection history. Female vendors and those with at least secondary education were more likely to practice good hygiene, including the use of protective gear (χ² = 13.89, p = 0.008) and regular handwashing. Hygiene practices were also significantly linked to sustainability indicators aligned with Balanced Scorecard domains, including staffing levels, income categories, and operational duration (p < 0.05). Vendors employing more staff reported higher income, and visibly hygienic practices were associated with customer loyalty and repeat purchases, highlighting hygiene as both a public health requirement and a driver of business sustainability. Conclusions: The findings indicate that hygiene functions not only as a public health requirement but also as a strategic business asset. Integrating behavioural drivers with performance metrics offers a practical framework for designing interventions that strengthen both public health protection and the sustainability of informal enterprises. Full article

Surface defect inspection of metallic lock components remains challenging due to strong specular reflections, low-contrast defect patterns, and geometric variability, which limit the consistency of manual inspection and conventional automated optical inspection (AOI) systems. This study presents an integrated visual inspection framework that combines controlled image acquisition with deep learning-based semantic segmentation to enable reliable and repeatable defect detection. A standardized rotational fixture with ring illumination was developed to stabilize imaging geometry, reduce reflection variability, and support consistent multi-view acquisition. A region-of-interest (ROI) masking strategy was further applied to suppress background interference and isolate the effective inspection region. At the algorithmic level, a Transformer-enhanced U-Net (TransU-Net) architecture was employed to jointly model local spatial features and global contextual dependencies, thereby improving boundary delineation and the detection of irregular surface anomalies. In addition, a boundary-aware weighted evaluation scheme was introduced to provide a more robust and application-relevant assessment by accounting for annotation uncertainty near defect edges. Experimental results demonstrate that the proposed method achieved an F1-score of 85.15%, with an average inference time of 0.3357 s per image for model prediction. Considering additional processes such as multi-view image acquisition, mechanical rotation, and preprocessing, the overall system-level inspection time is expected to be on the order of seconds per component in practical deployment. Full article

Autonomous task assignment in multi-unmanned aerial vehicle (UAV) systems operating in dynamic and safety-critical airspace environments is highly challenging due to complex spatial interactions and rapidly changing relative geometries. This paper proposes a hierarchical decision-making framework that bridges individual maneuvering behaviors with cooperative task allocation in multi-agent aerial systems. First, a high-fidelity single-agent maneuver model is learned using a physics-consistent simulation environment, where spatial advantage is evaluated based on relative distance and angular relationships within a kinematically feasible interaction zone (KIZ). Subsequently, a Geometry-Aware Graph Attention Network (GA-GAT) is developed to address scalable multi-agent assignment problems. Unlike conventional approaches that rely on flat feature representations, the proposed method explicitly incorporates kinematic feasibility constraints into the attention mechanism via a novel gating module, enabling efficient relational reasoning under dynamic conditions. The proposed framework is applicable to a range of civilian and safety-oriented scenarios, including UAV swarm coordination, emergency response monitoring, infrastructure inspection, and autonomous airspace management. Simulation results demonstrate that the GA-GAT-based approach significantly outperforms heuristic baselines in terms of coordination efficiency and overall system performance in complex multi-agent environments. This study highlights that decoupling maneuver-level control from high-level coordination provides a scalable and computationally efficient solution for real-time multi-UAV decision-making in safety-critical applications. The proposed framework is designed for general multi-agent coordination problems in civilian aerial applications. Full article

Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment). This article introduces the Random Generated Reference (RGR) method, a novel automated solution for quantifying marking degradation using a standard on-board camera. The proposed pipeline is a complete protocol from video acquisition to road marking inspection and validation of the inspection that combines deep learning with computer vision: YOLOv8 is employed for robust detection, while a unique algorithm generates a “perfect virtual reference” that dynamically replicates the real scene’s geometry and illumination conditions, including shadows. By computing pixel-level deviations between the observed marking and this ideal reference, the system assigns a continuous degradation score aligned with the UK CS126 standard. Experimental validation was conducted on a real-world circuit yielding over 20,000 detections. Verification via Cochran sampling demonstrates that 68% of the automated assessments fall within one class of human inspection. This proof-of-concept confirms the viability of an approach based on generating the ground truth and scene conditions—such as illumination, shadows, rain, traffic, etc.—for road marking inspection. Full article

Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect samples. Conventional deep learning methods, while accurate, often lack interpretability and exhibit low recall for rare defects. This paper proposes a novel hybrid system combining a Generative Adversarial Network (GAN), a Convolutional Neural Network (CNN), and Extreme Gradient Boosting (XGBoost 2.0.0) to enhance weld defect classification performance and transparency. Firstly, a Deep Convolutional GAN (DCGAN) creates synthetic images of the minority classes; thus, the problem of class imbalance is resolved. Then, a pretrained ResNet50V2 CNN is used to extract features of the deep layers from the original images as well as from the generated ones. After that, these features are fed into an XGBoost classifier, which uses tree-based learning to optimize classification results and make the process more understandable to the user. Furthermore, interpretation is also facilitated by Grad-CAM rendering of the CNN regions of interest and SHAP analysis to measure the involvement of the features in XGBoost. Experiments using the available LoHi-WELD datasets show that the overall accuracy is significantly improved, the per-class recall of the rare defects is also enhanced, and the robustness is also improved. The proposed hybrid method not only achieves better results but also generates visual/explainable output, which is very valuable when the system is implemented in industrial welding inspection systems. This paper serves as a liaison between the latest AI technology and the practical interpretability requirements of the mechanical and welding engineering fields. Full article

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of $8\pm 1.2$, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios. Full article