Search Results (295)

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

Steel surface defect detection is a crucial step in ensuring industrial production quality. However, due to significant variations in scale and irregular geometric morphology of steel surface defects, existing detection algorithms show notable deficiencies in multi-scale feature representation and cross-layer multi-scale feature fusion efficiency. To address these challenges, this paper proposes an improved real-time steel surface defect detection model, NGD-YOLO, based on YOLOv5s, which achieves fast and high-precision defect detection under relatively low hardware conditions. Firstly, a lightweight and efficient Normalization-based Attention Module (NAM) is integrated into the C3 module to construct the C3NAM, enhancing multi-scale feature representation capabilities. Secondly, an efficient Gather–Distribute (GD) mechanism is introduced into the feature fusion component to build the GD-NAM network, thereby effectively reducing information loss during cross-layer multi-scale information fusion and adding a small target detection layer to enhance the detection performance of small defects. Finally, to mitigate the parameter increase caused by the GD-NAM network, a lightweight convolution module, DCConv, that integrates Efficient Channel Attention (ECA), is proposed and combined with the C3 module to construct the lightweight C3DC module. This approach improves detection speed and accuracy while reducing model parameters. Experimental results on the public NEU-DET dataset show that the proposed NGD-YOLO model achieves a detection accuracy of 79.2%, representing a 4.6% mAP improvement over the baseline YOLOv5s network with less than a quarter increase in parameters, and reaches 108.6 FPS, meeting the real-time monitoring requirements in industrial production environments. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

A properly functioning capillary microcirculation is essential for sufficient oxygen and nutrient delivery to the central nervous system. The physical mechanisms governing the transport of red blood cells (RBCs) inside the narrow and irregularly shaped capillary lumen are complex, but understanding them is essential for identifying the root causes of neurological disorders like cerebral ischemia, Alzheimer’s disease, and other neurodegenerative conditions such as concussion and cognitive dysfunction in systemic inflammatory conditions. In this work, we conducted numerical simulations of three-dimensional capillary models, which were acquired ex vivo from a mouse retina, to characterize RBC transport. We show how the spatiotemporal velocity of the RBCs deviates in realistic capillaries and equivalent cylindrical tubes, as well as how this profile is affected by hematocrit and red cell distribution width (RDW). Our results show a previously unprecedented level of RBC velocity fluctuations in capillaries that depends on the geometric features of different confinement regions and a capillary circularity index $\left(I_{cc}\right)$ that represents luminal irregularity. This velocity fluctuation is aggravated by high hematocrit conditions, without any further effect on RDW. These results can provide a better understanding of the underlying mechanisms of pathologically high capillary transit time heterogeneity that results in microcirculatory dysfunction. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

Wheel polygonal wear of metro deteriorates the vibration environment of the vehicle system, potentially leading to resonance-induced fatigue failure of components. This poses serious risks to operational safety and increases maintenance costs. To address the adverse effects of wheel polygonal wear, dynamic tracking tests and numerical simulations were conducted. The modal analysis focused on the vehicle–track coupling system, incorporating various track structures to explore the formation mechanisms and key influencing factors of polygonization. Test results revealed dominant polygonal wear patterns of the seventh to ninth order, inducing forced vibrations in the 50–70 Hz frequency range. These frequencies closely match the P2 resonance frequency generated by wheel–rail interaction. When vehicle–track coupling is considered, the track’s frequency response shows multiple peaks within this range, indicating susceptibility to resonance excitation. Additionally, rail joint irregularities act as geometric excitation sources that trigger polygonal development, while the P2 force resonance mode plays a critical role in its amplification. Full article

Monitoring urban growth through simulation models is becoming increasingly vital for the sustainable management of cities. Although various raster-based models have been developed over the past three decades, the irregular, fragmented, and heterogeneous geometric structure of urban areas poses significant challenges to effectively modeling complex land use and land cover (LULC) transitions. To address these limitations, this study proposes a novel urban growth simulation model based on vector cellular automata (VCA). In this model, dynamic neighborhood relationships are flexibly established using an algorithm called growth vectors (GVs). Open-access data from four time periods between 1990 and 2018 were utilized for three major European metropolitan areas: Istanbul, Berlin, and Madrid. During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth. Full article

Strongly driven fluid and combustion systems typically contain a few, nonlinearly coupled, major flow constituents. It is necessary to identify the flow constituents in order to establish the underlying dynamics and to control these complex flows. Due to non-trivial boundary condition in realistic systems and long-range coupling, it is often difficult to construct accurate models of large-scale reacting systems. The question then arises if these flow constituents can be identified and controlled through analysis of experimental data. The difficulties in such analyses originate in the presence of high levels of noise and irregularities in the flow. A typical time series contains high-frequency noise as well as low-frequency features originating from the near translational invariance of the underlying fluid systems. We propose a pair of approaches to study such data. The first is the use of auto and cross correlation functions. Auto-correlation functions of the time series from a single transducer can be used effectively to demonstrate the low dimensionality of the flow. Second, we show that multi-point time series from appropriately placed transducers can be used to establish spatial characteristics of these flow constituents. The novelty of the approaches lies in the establishment of geometric and dynamic features of the primary flow constituents based on sensor data only, without the need of expensive imaging tools. These methods can potentially identify changes in flow behavior within complex propulsion systems, such as aircraft engines, by utilizing data collected from embedded transducers. Full article

In modern manufacturing, ensuring quality control for geometric features is critical, yet detecting anomalies in circular components remains underexplored. This study proposes a real-time defect detection framework for metal parts with holes, optimized for deployment on a Raspberry Pi 5 edge device. We fine-tuned and evaluated three deep learning models ResNet50, EfficientNet-B3, and MobileNetV3-Large on a grayscale image dataset (43,482 samples) containing various hole defects and imbalances. Through extensive data augmentation and class-weighting, the models achieved near-perfect binary classification of defective vs. non-defective parts. Notably, ResNet50 attained 99.98% accuracy (precision 0.9994, recall 1.0000), correctly identifying all defects with only one false alarm. MobileNetV3-Large and EfficientNet-B3 likewise exceeded 99.9% accuracy, with slightly more false positives, but offered advantages in model size or interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that each network focuses on meaningful geometric features (misaligned or irregular holes) when predicting defects, enhancing explainability. These results demonstrate that lightweight CNNs can reliably detect geometric deviations (e.g., mispositioned or missing holes) in real time. The proposed system significantly improves inline quality assurance by enabling timely, accurate, and interpretable defect detection on low-cost hardware, paving the way for smarter manufacturing inspection. Full article

LiDAR-based 3D object detection is fundamental in autonomous driving but remains challenging due to the irregularity, unordered nature, and non-uniform density of point clouds. Existing methods primarily rely on either graph-based or tree-based representations: Graph-based models capture fine-grained local geometry, while tree-based approaches encode hierarchical global semantics. However, these paradigms are often used independently, limiting their overall representational capacity. In this paper, we propose density-aware tree–graph cross-message passing (DA-TGCMP), a unified framework that exploits the complementary strengths of both structures to enable more expressive and robust feature learning. Specifically, we introduce a density-aware graph construction (DAGC) strategy that adaptively models geometric relationships in regions with varying point density and a hierarchical tree representation (HTR) that captures multi-scale contextual information. To bridge the gap between local precision and global contexts, we design a tree–graph cross-message-passing (TGCMP) mechanism that enables bidirectional interaction between graph and tree features. The experimental results of three large-scale benchmarks, KITTI, nuScenes, and Waymo, show that our method achieves competitive performance. Specifically, under the moderate difficulty setting, DA-TGCMP outperforms VoPiFNet by approximately 2.59%, 0.49%, and 3.05% in the car, pedestrian, and cyclist categories, respectively. Full article

This study proposes a unified span–depth ratio model aimed at optimizing the minimum thickness of reinforced concrete flat plate slabs, addressing the limitations of the simplified span-to-depth ratio provisions in ACI 318. The existing code does not fully consider critical parameters such as panel aspect ratio, reinforcement ratio, support conditions, concrete strength, and long-term deflections due to creep and shrinkage. To overcome these shortcomings, a generalized analytical model is developed based on fundamental deflection theory, incorporating both immediate and time-dependent behaviors. The model is validated through numerical simulations applied to interior, edge, and corner slab panels subjected to various geometric configurations, loading scenarios, and reinforcement levels. Results from the parametric study indicate that deflection control improves significantly with higher reinforcement ratios and lower aspect ratios, leading to more efficient slab designs. Comparisons with ACI 318 guidelines reveal that the proposed model provides enhanced accuracy, particularly for irregular slab geometries and stringent deflection limits (e.g., L/480). The findings highlight that conventional code-based thickness limits may underestimate slab depth requirements in many practical scenarios. The study advocates for integrating deflection-based considerations into the preliminary design stage, offering structural engineers a more robust and practical tool to ensure serviceability while optimizing material use. Full article

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. A novel exponential moving average unit (EMAU) enhances feature robustness by adaptively weighting historical states, while morphology-aware augmentation simulates variable road widths and spectral noise. Evaluations on cross-domain datasets demonstrate state-of-the-art performance with 92.16% precision, 80.77% F1-score, and 67.75% IoU (intersection over union), outperforming baseline models by 4.3% in precision and reducing annotation dependency by 94.6%. By reducing per-kilometer operational costs by 78% relative to LiDAR (Light Detection and Ranging) alternatives, OMRoadNet establishes a practical solution for intelligent mining infrastructure mapping, bridging the critical gap between structured urban datasets and unstructured mining environments. Full article

Defect repair on custom-curved glulam beams is still performed manually because knots are irregular, numerous, and located on elements that cannot pass through linear production lines, limiting the scalability of timber-based architecture. This study presents Woodot, an autonomous mobile robotic platform that combines an omnidirectional rover, a six-dof collaborative arm, and a fine-tuned Segment Anything computer vision pipeline to identify, mill, and plug surface knots on geometrically variable beams. The perception model was trained on a purpose-built micro-dataset and reached an F1 score of 0.69 on independent test images, while the integrated system located defects with a 4.3 mm mean positional error. Full repair cycles averaged 74 s per knot, reducing processing time by more than 60% compared with skilled manual operations, and achieved flush plug placement in 87% of trials. These outcomes demonstrate that a lightweight AI model coupled with mobile manipulation can deliver reliable, shop-floor automation for low-volume, high-variation timber production. By shortening cycle times and lowering worker exposure to repetitive tasks, Woodot offers a viable pathway to enhance the environmental, economic, and social sustainability of digital timber construction. Nevertheless, some limitations remain, such as dependency on stable lighting conditions for optimal vision performance and the need for tool calibration checks. Full article