Search Results (486)

An accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inherent limitations. Specifically, methods that rely on fitting planar contours using depth-discontinuous points are prone to systematic errors, which hinder the precise extraction of the 3D positions of feature points. This, in turn, compromises the accuracy and robustness of the calibration. To overcome these challenges, this paper introduces a novel 3D calibration plate incorporating the gradient depth, localization markers, and corner features. At the point cloud level, the gradient depth enables the accurate estimation of the 3D coordinates of feature points. At the image level, corner features and localization markers facilitate the rapid and precise acquisition of 2D pixel coordinates, with minimal interference from environmental noise. This method establishes a rigorous and systematic framework to enhance the accuracy of LiDAR–camera extrinsic calibrations. In a simulated environment, experimental results demonstrate that the proposed algorithm achieves a rotation error below 0.002 radians and a translation error below 0.005 m. Full article

To promote the automation and intelligence of rail freight, the accurate identification and localization of bulk cargo unloading hoppers have become a key technical challenge. Under the technological wave driven by the deep integration of Industry 4.0 and artificial intelligence, the bulk cargo unloading process is undergoing a significant transformation from manual operation to intelligent control. In response to this demand, this paper proposes a vision-based 3D localization system for unloading hoppers, which adopts a single visual sensor architecture and integrates three core modules: object detection, corner extraction, and 3D localization. Firstly, a lightweight hybrid attention mechanism is incorporated into the YOLOv5 network to enable edge deployment and enhance the detection accuracy of unloading hoppers in complex industrial scenarios. Secondly, an image processing approach combining depth consistency constraint (DCC) and geometric structure constraints is designed to achieve sub-pixel level extraction of key corner points. Finally, a real-time 3D localization method is realized by integrating corner-based initialization with an RGB-D SLAM tracking mechanism. Experimental results demonstrate that the proposed system achieves an average localization accuracy of 97.07% under challenging working conditions. This system effectively meets the comprehensive requirements of automation, intelligence, and high precision in railway bulk cargo unloading processes, and exhibits strong engineering practicality and application potential. Full article

Digital twin models utilising point cloud data have received significant attention for efficient bridge maintenance and performance assessment. There are some studies that show finite element (FE) models from point cloud data. While most of those approaches focus on modelling by solid elements, modelling of some civil structures, such as bridges, requires various uses of beam and shell elements. This study proposes a systematic approach for constructing shell element FE models from point cloud data of thin-walled structural members. The proposed methodology involves k-means clustering for point cloud segmentation into individual plates, principal component analysis for neutral plane estimation, and edge detection based on normal vector variations for geometric structure determination. Validation experiments using point cloud data of a steel corner specimen revealed dimensional errors up to 5 mm and angular errors up to 6°, but static load analysis demonstrated good accuracy with maximum displacement errors within 3.8% and maximum stress errors within 7.7% compared to nominal models. Additionally, the influence of point cloud data quality on FE model geometry and analysis results was evaluated based on geometric accuracy and point cloud density metrics, revealing that significant variations in density within the same surface lead to reduced neutral plane estimation accuracy. Furthermore, toward practical application to actual bridge structures, on-site measurements and quality evaluation of point cloud data from a steel plate girder bridge were conducted. The results showed that thickness errors in the bridge data reached up to 2 mm, while surface deviation RMSE ranged from 3 to 5 mm. This research contributes to establishing practical FE modelling procedures from point cloud data and providing a model validation framework that ensures appropriate abstraction in structural analysis. Full article

For the local oscillation phenomenon of the APF algorithm in the face of static U-shaped obstacles, the path cusp phenomenon caused by the vehicle corner and path curvature constraints is not taken into account, as well as the low path safety caused by ignoring the vehicle volume constraints. Therefore, an APF-Dijkstra path planning fusion algorithm based on steering model and volume constraints is proposed to improve it. First, perform an expansion treatment on the obstacles in the map, optimize the search direction of the Dijkstra algorithm and its planned global path, ensuring that the distance between the path and the expanded grid is no less than 1 m, and use the path points as temporary target points for the APF algorithm. Secondly, a Gaussian function is introduced to optimize the potential energy function of the APF algorithm, and the U-shaped obstacle is ellipticized, and a virtual target point is used to provide the gravitational force. Again, the three-point arc method based on the steering model is used to determine the location of the predicted points and to smooth the paths in real time while constraining the steering angle. Finally, a 4.5 m × 2.5 m vehicle rectangle is used instead of the traditional mass points to make the algorithm volumetrically constrained. Meanwhile, a model for detecting vehicle collisions is established to cover the rectangle boundary with 14 envelope circles, and the combined force of the computed mass points is transformed into the combined force of the computed envelope circles to further improve path safety. The algorithm is validated by simulation experiments, and the results show that the fusion algorithm can avoid static U-shaped obstacles and dynamic obstacles well; the curvature change rate of the obstacle avoidance path is 0.248, 0.162, and 0.169, and the curvature standard deviation is 0.16, which verifies the smoothness of the fusion algorithm. Meanwhile, the distances between the obstacles and the center of the rear axle of the vehicle are all higher than 1.60 m, which verifies the safety of the fusion algorithm. Full article

A biharmonic boundary value problem with a singularity is one of the mathematical models of processes in fracture mechanics. It is necessary to have estimates of the function norms in the neighborhood of the singularity point to study the existence and uniqueness of the $R_{\nu }$-generalized solution, its coercive and differential properties of biharmonic boundary value problems with a corner singularity. This paper establishes estimates of a function in the neighborhood of a singularity point in the norms of weighted Lebesgue spaces through its norms in weighted Sobolev spaces over the entire domain, with a minimum weight exponent. In addition, we obtain an estimate of the function norm in a boundary strip for the degeneration of a function on the entire boundary of the domain. These estimates will be useful not only for studying differential problems with singularity, but also in estimating the convergence rate of an approximate solution to an exact one in the weighted finite element method. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

We investigate the behavior of solutions of second-order elliptic Dirichlet problems for a convex domain by using a “gliding hump” technique and prove that there are no contour limits at a specified point of the boundary of the domain. Then we consider two-dimensional domains which have a reentrant (i.e., nonconvex) corner at a point P of the boundary of the domain. Assuming certain comparison functions exist, we prove that for any solution of an appropriate Dirichlet problem on the domain whose graph has finite area, there are infinitely many curves of finite length in the domain ending at P along which the solution has a limit at P. We then prove that such behavior occurs for quasilinear operations with positive genre. Full article

We present a compact and cost-effective real-time PCR system designed for point-of-care testing (POCT), utilizing a PCB-based disposable chip and an open-platform CMOS camera. The system integrates precise thermal cycling with software-synchronized fluorescence detection and provides real-time analysis through a dedicated user interface. To minimize cost and complexity, a polycarbonate reaction chamber was integrated with a PCB-based heater and thermistor. A slanted LED illumination setup and an open-platform USB camera were employed for fluorescence imaging. Signal alignment was enhanced using device-specific region-of-interest (ROI) tracking based on copper pad corner detection. Thermal cycling performance achieved a heating rate of 8.0 °C/s and a cooling rate of −9.3 °C/s, with steady-state accuracy within ±0.1 °C. Fluorescence images exhibited high dynamic range without saturation, and the 3σ-based ROI correction method improved signal reliability. System performance was validated using Chlamydia trachomatis DNA standard (10³ copies), yielding consistent amplification curves with a Ct standard deviation below 0.3 cycles. These results demonstrate that the proposed system enables rapid, accurate, and reproducible nucleic acid detection, making it a strong candidate for field-deployable molecular diagnostics. Full article

The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2–0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios. Full article

This paper presents a fully integrated bandpass filter (BPF) with high tunability based on a novel differential active inductor (DAI), designed for sensor interface circuits operating in the ultra-high frequency (UHF) band. The design of the proposed DAI is based on a symmetrical configuration, utilizing a differential amplifier for the feedforward transconductance and a common-source (CS) transistor for the feedback transconductance. By integrating a cascode scheme with a feedback resistor, the quality factor of the active inductor is significantly improved, leading to enhanced mid-band gain for the bandpass filter. To facilitate independent tuning of the BPF‘s center frequency and mid-band gain, an active resistor adjustment and bias voltage control are employed, providing precise control over the filter’s operational parameters. Post-layout simulations and process corner results are conducted with 0.13 µm CMOS technology at 1.2 V supply voltage. The proposed second order BPF achieves a broad tuning range of 280 MHz to 2.426 GHz, with a passband gain between 8.9 dB and 16.54 dB. The design demonstrates a maximum noise figure of 16.54 dB at 280 MHz, an input-referred 1 dB compression point of −3.78 dBm, and a third-order input intercept point (IIP3) of −0.897 dBm. Additionally, the BPF occupies an active area of only 68.2×30 µm², including impedance-matching part, and consumes a DC power of 14–20 mW. The compact size and low power consumption of the design make it highly suitable for integration into modern wireless sensor interfaces where performance and area efficiency are critical. Full article

This paper introduces a new methodology that enables the production of polymer–metal parts through hybrid additive manufacturing. The approach combines fused filament fabrication (FFF) of polymers with adhesive bonding of metal inserts, applied during layer-by-layer construction. The work is based on unit cells designed and fabricated using eco-friendly materials—polylactic acid (PLA) and aluminum—which were subsequently analyzed for build quality and for mechanical performance under tensile lap-shear and three-point bending tests. The acquired knowledge in terms of optimal processing parameters for attaining strong polymer–metal bonds was then applied for the fabrication and testing of prototypes representing modular corner connectors for framing applications. Results on build quality demonstrate that issues, such as lumps and warping, can be solved by finetuning the 3D printing stages of the proposed methodology. In terms of destructive testing, significant improvements in the mechanical performance of PLA can be achieved, demonstrating the feasibility of the proposed methodology in integrating the lightweight properties of polymers with the stiffness of metals. This enables the development of innovative, sustainable and eco-friendly solutions that align with the growing demand for eco-friendly materials and processes in manufacturing. Full article

Compared to a single horizontal or inclined tunnel, a U-shaped underwater tunnel combines both types. If such tunnels catch fire, the resulting scenarios will lead to varying intensities of induced airflow, which significantly impact the internal heat transfer mechanisms. This study numerically simulated the effects of varying induced airflow strengths on the heat transfer proportion and temperature distribution within the tunnel. Key variables including the inclination angle of tunnel sections, the heat release rate (HRR) of the fire source, and its distance to the tunnel opening were systematically investigated. The results indicate that when the fire is in the horizontal tunnel section, the primary factor affecting the temperature field distribution is the HRR of the fire. As the fire source moves toward the inclined tunnel section, a transition to strong induced airflow occurs above the corner at low angles of inclination. As the slope increases, the transition position shifts downstream, toward the lower side of the corner. At this point, the tunnel is affected by strong induced airflow. Using dimensionless analysis, models were developed for temperature field distribution under strong and weak induced airflow, guiding underwater tunnel spray activation temperature and firefighting and rescue efforts. Full article

Target detection helps to identify, locate, and monitor key components and potential issues in power sensing networks. The fusion of infrared and visible light images can effectively integrate the target the indication characteristics of infrared images and the rich scene detail information of visible light images, thereby enhancing the ability for target detection in power equipment in complex environments. In order to improve the registration accuracy and feature extraction stability of traditional registration algorithms for infrared and visible light images, an image registration method based on an improved SIFT algorithm is proposed. The image is preprocessed to a certain extent, using edge detection algorithms and corner detection algorithms to extract relatively stable feature points, and the feature vectors with excessive gradient values in the normalized visible light image are truncated and normalized again to eliminate the influence of nonlinear lighting. To address the issue of insufficient deep information extraction during image fusion using a single deep learning network, a dual ResNet network is designed to extract deep level feature information from infrared and visible light images, improving the similarity of the fused images. The image fusion technology based on the dual ResNet network was applied to the target detection of sensing insulators in the power sensing network, improving the average accuracy of target detection. The experimental results show that the improved registration algorithm has increased the registration accuracy of each group of images by more than 1%, the structural similarity of image fusion in the dual ResNet network has been improved by about 0.2% compared to in the single ResNet network, and the mean Average Precision (mAP) of the fusion image obtained via the dual ResNet network has been improved by 3% and 6% compared to the infrared and visible light images, respectively. Full article

Traditional construction of timber houses in Tanzania has been prevalent for years; however, inhabiting these structures has been a challenge due to the instability of the buildings under various loadings. This instability, despite its lightweight, is mainly controlled by mechanical joints within timber members. Parametric Python scripts were developed in Abaqus (version 6.13) to have a reliable joint between timber volume modules and assess their response when subjected to wind forces. Two timber volume modules, each with a height of 3.0 m, were subjected to a horizontal displacement of 10 mm. Results show that the screwed fasteners between the modules result in high shear resistance due to the embedded fastener’s threads in timber members increasing the rope effect. Additionally, with weak fastener stiffness, the openings in the longitudinal wall had no effect on resisting shear compared to strong joints between modules. Longitudinal walls with doors and window openings showed a decrease in shear force to 21.95 kN, which is 44% less than the 39 kN of walls without openings. In addition, for a single door in the wall, the shear force decreased to 17.9%, indicating that major shear forces in the wall are affected by the window opening due to its large size and proximity to the point of load application. Furthermore, the stresses were concentrated in the corners of the openings, subjecting the structure to failure during its in-service life and demanding the use of cross-diagonal timber members between the corners to redistribute corner stresses. It is recommended that these types of houses be adopted due to less slip deformation (less than 10 mm) caused by wind speed of 24 km/h. Full article

The advancement of SAR satellites enables continuous and real-time ship monitoring on water surfaces regardless of time and weather. Traditional ship detection algorithms in SAR imagery using manually designed operators lack accuracy, while many existing deep learning-based detection algorithms are computationally intensive and have room for accuracy improvement. Inspired by CenterNet, we propose the Three Points Network (TPNet). It locates the ship’s center point and estimates distances to the top-left and bottom-right corners for precise positioning. We introduce several innovative mechanisms to enhance TPNet’s performance, improving both accuracy and computational efficiency. Evaluated on the open-source SAR-Ship-Dataset, TPNet outperforms 14 other deep learning-based detection algorithms in accuracy and efficiency. Its strong generalization ability is further verified on SSDD and HRSID datasets. These results show TPNet’s potential in real-time maritime surveillance and monitoring systems. Full article