Search Results (3,147)

Heliostats constitute essential elements within concentrating solar power (CSP), where their structure, load profiles, and operational environment render wind loads a critical factor in their design considerations, as these loads directly impact the cost of energy generation. The aerodynamics significantly influence wind-induced effects, resulting in considerable variability in wind loads among different heliostat geometries. This study utilizes the Computational Fluid Dynamics (CFD) methodology to systematically examine the aerodynamic behavior of an isolated pentagonal heliostat. Employing the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations with an atmospheric boundary layer inlet condition, the investigation focuses on the flow field and wind load characteristics at four representative pitch angles: 0° (stow position), 30°, 60°, and 90°. Findings indicate that the pitch angle exerts a decisive impact on flow separation patterns. Specifically, as the elevation angle decreases, the flow regime shifts from being predominantly influenced by the mirror surface to being governed by the support structure, mediated through an interactive coupling between these components. At the 60° operational pitch angle, the pentagonal heliostat’s distinctive corner geometry induces an asymmetric vortex configuration—characterized by a smaller vortex at the top and a larger one at the bottom—thereby disrupting the conventional vortex distribution observed in symmetric heliostat designs. A further analysis of wind load characteristics indicates that, compared to a quadrilateral heliostat, the pentagonal mirror exhibits a significantly lower Elevation Moment Coefficient, despite a slight increase in the normal force coefficient. This reduction is attributed to a balancing mechanism: the “vortex structure asymmetry” creates an upper-large–lower-small distribution of absolute negative pressure on the support surface, while the “stagnation point position” shift with elevation angle produces an upper-small–lower-large distribution of absolute positive pressure on the reflector. The interaction between these opposing trends minimizes the net pressure differential across the mirror height, thereby contributing to superior overall aerodynamic performance. The reduction in the elevation moment coefficient contributes to enhanced structural wind resistance, thereby improving the overall energy efficiency and economic viability of concentrating solar power. Full article

The von Neumann architecture faces severe bottlenecks in energy efficiency. Computing-in-Memory (CiM) addresses this by performing computations within memory arrays, yet analog CiM solutions suffer from precision loss and high overhead from analog-to-digital converters and digital-to-analog converters (ADCs/DACs). This paper proposes a novel ADC-free CiM architecture based on Ferroelectric Field-Effect Transistors (FeFETs). Logic circuits (NOR, NAND, XNOR) that store weight vectors within FeFETs were designed. Compared with analog CiM circuits, the FeFETs-CiM circuits proposed in this paper can reduce power consumption by 901.1 times and latency by 272.7 times. Furthermore, the design of 3-bit FeFETs-CiM gates was extended, demonstrating flexible configurability for scalable edge computing applications. Finally, an application specific FeFETs-CiM subtractor for k-nearest neighbor (kNN) distance calculation was designed, which energy consumption is as low as 85.02 fJ/OP and latency is as low as 0.56 ns under 500 MHz operation frequency. The calculation robustness of the FeFETs-CiM kNN distance calculator was ensured by simulating under different process corners and temperatures. The performance improvements owing to the proposed FeFETs-CiM CMOS circuits were evaluated by taking the kNN algorithm as an example, which can ensure the data access reduction by more than 300 times compared to von Neumann architecture. Full article

3D vision-based groove detection is playing a critical role in enabling autonomous recognition. However, most existing interference evaluation strategies rely on height-based statistics or handcrafted heuristics, which (i) confuse genuine groove geometries (e.g., deep gaps and rounded corners) with noise and (ii) are sensitive to measurement scale and scanning configurations, making parameter tuning unreliable across scenes. To address this challenge, this paper proposes a novel method for evaluating the degree of interference in groove detection data, providing a reliable basis for the adaptive adjustment of algorithm parameters. The method leverages the angles of reconstructed triangular patches to assess the interference level in groove 3D detection data and computes the eigenvalues of the covariance matrix of these angles, establishing a rotationally invariant model for interference quantification. Experimental results show that the proposed method outperforms traditional methods, identifying more regions of high dispersion and demonstrating better adaptability to common groove features, such as deep gaps and rounded corners. By exploiting geometric invariance as a form of symmetry, the proposed eigenvalue-based dispersion descriptor provides a robust and coordinate-independent criterion for interference evaluation. Quantitatively, across multiple real industrial datasets, the proposed descriptor achieves an average 30.99% improvement in identifying severely interfered regions compared with the mainstream height-difference-based evaluation baseline. Full article

Aboveground storage tanks are used to store various fluids and chemicals for many industrial purposes. According to API standard 653, the structural integrity of these tanks must be regularly assessed. The U.S. EPA requires each operator to have a Spill Prevention, Control and Countermeasure Plan (SPCC) for aboveground storage containers. The accepted practice for inspection of these tanks, particularly the tank bottoms, requires removing the tank from service, emptying the tank, and interior entry for direct inspection of the structure. The required inspection operations are hazardous due to the chemicals themselves as well as the requirement to operate within confined spaces. An inspection from outside the tank would have significant cost and time benefits and would provide a large reduction in the risks faced by inspection personnel. Guided wave (GW) testing is a promising candidate for screening of storage tank walls and bottoms from the tank exterior due to the ability of GWs to propagate over long distances from a fixed probe location. The lowest-order transverse-motion guided wave modes (e.g., torsional vibrations in pipes) are a good choice for long-range inspection because this mode is not dispersive; therefore, the wave packets do not spread out in time. A common weakness of guided wave inspection is the complexity of report generation in the presence of multiple geometry features in the structure, such as welds, welded plate corners, attachments and so on. In some cases, these features cause generation of non-relevant indications caused by mode conversion. Another significant challenge in applying GW testing is development of probes with high-enough signal amplitudes and relatively small footprints to allow them to be mounted on short tank bottom extensions. In this paper, a new generation of magnetostrictive transducers will be presented. The transducers are based on the reversed Wiedemann effect and can generate shear horizontal mode guided waves over a wide frequency range (20–150 kHz) with SNRs in excess of 50 dB. The recently developed SwRI MST 8 × 8 probe contains an array of eight pairs of individual magnetostrictive transducers (MsTs). The data acquisition hardware allows acquisition using Full Matrix Capture (FMC) and analysis software reporting of anomalies based on Total Focusing Method (TFM) image reconstruction. This novel inspection package allows generation of reports that map out corrosion locations and provide estimates of defect widths. Case studies of this technology on actual storage tank walls and bottoms will be presented together with validation of processing methods on mockups with known anomalies and geometry features. Full article

This study focuses on a critical issue in Heterotrophic Prestressed Concrete Pavement (HPCP), the closure pour, which is prone to weak interfacial bonding, stress concentration, and cracking under repeated aircraft loads. To overcome these shortcomings, a novel prefabricated integrated anchorage (PIA) device is designed, integrating the functions of both a tensioning end and an anchoring end. Based on the PIA, a continuous tensioning construction process is introduced, which eliminates the traditional closure pour by utilizing the casting space of the subsequent slab to tension the preceding one. Finite element analysis demonstrates that the PIA device exhibits complex stress alternation under prestressing, with the most critical cross sections located at depths of 100 to 150 mm. A parametric study further reveals a linear relationship between the tension angle and the maximum principal stress in the PIA. In the HPCP system, prestressing establishes a predominant compressive stress field in the slab, effectively enhancing crack resistance. However, localized stress concentration and tension–compression alternation occur not only around the PIAs but also notably at the slab corners. These results confirm that the PIA device and its associated continuous construction method not only overcome the drawbacks of closure pours but also provide an innovative, efficient, and sustainable technical pathway for improving the quality and performance of airfield pavement engineering. Full article

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

Due to the brittleness and volume sensitivity, segmentation is necessary for the cement concrete pavement slabs currently in widespread use to mitigate thermal stress and deformation. The dimensions of segmented pavement slabs are typically constrained to 4∼6 m, which results in a large number of joints. These joints cause damages such as corner spalling and fracture under the impact of repeated loads and environmental factors. In addition, maintenance costs are significantly increased due to the numerous joints. To enhance pavement performance and extend service lifespan, this paper proposes a design methodology for large pavement slabs. This method breaks the dimensional constraint and significantly reduces the number of joints, thereby improving comfort and durability, lowering maintenance costs, and meeting the operational requirements of new aircraft types. In this paper, pavement slab thermal stress is divided into curling stress and thermal expansion stress according to different deformation types. The diurnal and annual distributions of these two types of stresses are also investigated. Moreover, the maximum dimension design of pavement slabs comprehensively considers aircraft loads, thermal stresses, and fatigue characteristics. The results indicate that the diurnal and annual distributions of curling and thermal expansion stresses exhibit sinusoidal patterns. Under different temperature gradients and slab thicknesses, the allowable maximum slab dimension is presented. It is feasible to break the 4∼6 m limit for the maximum dimension of the pavement slab, which provides a new reference for improving pavement performance and lifespan. Full article

Autism Spectrum Disorder (ASD) is a neurological condition that affects communication and social interaction skills, with individuals experiencing a range of challenges that often require specialized care. Automated systems for recognizing ASD face significant challenges due to the complexity of identifying distinguishing features from facial images. This study proposes an incremental advancement in ASD recognition by introducing a dual-stream model that combines handcrafted facial-landmark features with deep learning-based pixel-level features. The model processes images through two distinct streams to capture complementary aspects of facial information. In the first stream, facial landmarks are extracted using MediaPipe (v0.10.21),with a focus on 137 symmetric landmarks. The face’s position is adjusted using in-plane rotation based on eye-corner angles, and geometric features along with 52 blendshape features are processed through Dense layers. In the second stream, RGB image features are extracted using pre-trained CNNs (e.g., ResNet50V2, DenseNet121, InceptionV3) enhanced with Squeeze-and-Excitation (SE) blocks, followed by feature refinement through Global Average Pooling (GAP) and DenseNet layers. The outputs from both streams are fused using weighted concatenation through a softmax gate, followed by further feature refinement for classification. This hybrid approach significantly improves the ability to distinguish between ASD and non-ASD faces, demonstrating the benefits of combining geometric and pixel-based features. The model achieved an accuracy of 96.43% on the Kaggle dataset and 97.83% on the YTUIA dataset. Statistical hypothesis testing further confirms that the proposed approach provides a statistically meaningful advantage over strong baselines, particularly in terms of classification correctness and robustness across datasets. While these results are promising, they show incremental improvements over existing methods, and future work will focus on optimizing performance to exceed current benchmarks. Full article

High-platform timber structures represent a typical structural form in ancient Chinese architecture, where the platform and the upper timber structure constitute a mechanically coupled system with interacting mechanical properties and response behaviors. However, a systematic understanding of their global coupling mechanism and its impact on structural response remains unclear. This study investigates a representative high-platform timber structure, i.e., Xi’an Bell Tower, to analyze the static and dynamic response characteristics of the platform–superstructure system using in situ dynamic testing and finite element simulation. The results indicate that the simulated first two natural frequencies align well with in situ measurements, validating the model’s rationality. The global coupling effect alters the system’s mass and stiffness distribution, leading to an overall lengthening of the structural natural periods. Structural self-weight is identified as the dominant factor inducing vertical deformation under serviceability conditions, with significant deformation observed at the platform’s edges and corners. Under lateral loads, deformations concentrate in the second story of the timber superstructure, with seismic actions exerting a more pronounced influence than wind loads. Under rare earthquake conditions, the maximum inter-story drift ratio reaches 1/70. Local tensile stresses at the joints, architrave ends, and the Dou-Gong layer exceed the timber’s tensile strength parallel to the grain, identifying these components as the weak links in the structure’s seismic performance. Full article

Laser-cladding directed energy deposition enables both the repair and fabrication of complex metallic components with curved surfaces. However, during multi-axis deposition on curved substrates, sharp transient feed-rate fluctuations at corner segments—together with an approximately constant powder feed rate—readily cause local over-deposition and geometric defects (e.g., nodules and humps). These defects compromise surface-profile fidelity, thereby creating a major barrier to practical deployment. To overcome this limitation, we propose a corner-oriented path-optimization strategy based on geometric code parsing. By operating directly on the toolpath without modifying the Computer-Aided Design model or slicing workflow, the proposed method suppresses corner overbuild and associated morphological distortion in curved-surface directed energy deposition, substantially improving dimensional consistency and surface quality. Overall, this strategy provides a scalable and broadly applicable route toward high-precision, high-reliability, industrial-scale curved-surface additive manufacturing. Full article

Building polygon extraction is a critical task in remote sensing analysis and a fundamental component of modern urban management. Conventional segmentation-based methods often suffer from geometric distortions during the conversion from masks to polygons. End-to-end polygon prediction approaches (e.g., PolyWorld) alleviate this issue by directly predicting building polygons; however, existing PolyWorld-like methods remain limited in accurate corner vertex detection and polygon reasoning due to insufficient representation learning, particularly for geometry. In this work, we propose PolyGeom, an end-to-end framework equipped with a geometry-aware graph transformer for accurate and robust building polygon extraction. PolyGeom employs the Segment Anything Model (SAM) as its backbone to leverage large-scale pretrained features, thereby capturing both local and global semantics. Moreover, we propose a geometry-aware graph transformer that explicitly models geometry of building polygons, facilitating more reliable polygon reasoning. Extensive experiments on three challenging benchmarks, CrowdAI, WHU, and BONAI datasets, demonstrate that PolyGeom consistently outperforms existing methods in terms of building detection accuracy, topology correctness, and geometry alignment. Ablation studies further validate the effectiveness of the two key proposed designs in building polygon extraction. Full article

With the advancement of synthetic aperture radar (SAR) sensor technology, linear structures such as building facades have become increasingly discernible in SAR imagery. Accurate detection of these line features is critical for object recognition and 3D model reconstruction. To the best of our knowledge, few existing methods explicitly address the problem of detecting lines composed of point scatterers. In this paper, we analyze the characteristics of such lines and propose a novel point scatterer-driven growth-based approach, termed PSG-Line, for their detection. Point scatterers are first extracted by combining the ordered-statistics constant false alarm rate (OS-CFAR) algorithm with non-maximum suppression and Harris corner response thresholding. Line segments are then initiated from these scatterers and iteratively extended by incorporating subsequent points that satisfy a set of geometric constraints. Finally, the detected line segments are validated based on the Helmholtz principle. Local principal orientations of point scatterers are estimated and incorporated into the line segment growth and validation stages. Both simulation and real-life SAR data experiments demonstrate that the PSG-Line algorithm outperforms existing line detection methods in accurately detecting lines composed of point scatterers. Full article

The effect of helmet shape on aerodynamic drag is numerically investigated when cyclists lean during cornering in individual cycling races. Five helmet models (H1–H5) with varying curvatures are constructed, and under the conditions of a vehicle speed of 20 m/s and a 45° body inclination, the SST k-ω turbulence model and grid independence verification (final grid count: 6.75 million) are used to systematically analyze the distribution of velocity, vortex, pressure, and wall shear stress fields. The results show that increasing helmet curvature enlarges the windward area, intensifies rear vortex strength, slows pressure recovery, and ultimately increases drag. The H3 helmet is identified as the optimal choice for individual races due to its stable flow field and minimum drag (268.4 N). Further analysis of different initial speeds (5–25 m/s) reveals that as speed increases, the boundary layer velocity gradient rises, with wall shear stress (0–5 Pa) and drag (100–500 N) also increasing accordingly, while the pressure field decreases gradually due to the Bernoulli effect. Full article

Bi-doped low-silver Sn-Ag-Cu solders are increasingly gaining attention in advanced electronic packaging due to their cost-effectiveness and enhanced mechanical properties. However, the thermo-mechanical reliability mechanisms of such modified solders, particularly Sn-0.3Ag-0.7Cu-3Bi (SAC0307-3Bi) within Ball Grid Array (BGA) assemblies, remain insufficiently understood. To address this gap, this research proposes a comprehensive assessment framework integrating constitutive parameter calibration with finite element analysis (FEA) to accurately characterize the mechanical behavior and fatigue durability of SAC0307-3Bi solder joints under cyclic thermal loads. The Anand viscoplastic parameters were first calibrated via the Norton creep law and virtual tensile tests. Subsequently, a 3D quarter-symmetry model was constructed to replicate thermal cycling conditions between 25 °C and 125 °C. Simulation data reveal a strong correlation between stress concentration and the Distance to Neutral Point (DNP), pinpointing the chip-side interface of the corner joint as the critical failure site. Moreover, creep strain was observed to accrue in a “step-wise” pattern, predominantly during the heating and cooling ramps, reflecting distinct temperature sensitivity. Utilizing the Syed model, the fatigue life was estimated at approximately 2239 cycles. These insights serve as a crucial benchmark for designing robust packages using Bi-doped, low-silver lead-free solders. Full article

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article