Search Results (154)

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Ice accumulation on exposed surfaces presents substantial economic and safety challenges across various industries. To overcome limitations associated with traditional anti-icing methods, such as the use of nanoparticles, this study introduces a novel and facile approach for fabricating superhydrophobic and anti-icing microstructures using cost-effective LCD 3D printing technology. The influence of diverse pillar geometries, including square, cylindrical, hexagonal, and truncated conical forms, was analyzed to assess their effects on the hydrophobic and anti-icing/icephobic performance in terms of wettability, ice adhesion strength, and icing delay time. The role of microstructure topography was further investigated through cylindrical patterns with varying geometric parameters to identify optimal designs for enhancing hydrophobic and icephobic characteristics. Furthermore, the effectiveness of surface functionalization using a low surface energy material was evaluated. Our findings demonstrate that the synergistic combination of tailored microscale geometries and surface functionalization significantly enhances anti-icing performance with reliable repeatability, achieving ice adhesion of 13.9 and 17.9 kPa for square and cylindrical pillars, respectively. Critically, this nanoparticle-free 3D printing and low surface energy treatment method offers a scalable and efficient route for producing high-performance hydrophobic/icephobic surfaces, opening promising avenues for applications in sectors where robust anti-icing capabilities are crucial, such as renewable energy and transportation. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solution for precise road intersection mapping that eliminates common digitization errors. Unlike conventional approaches that only detect intersection positions, our method systematically reconstructs complete intersection geometries while maintaining topological consistency. The technique combines plane surveying principles (including line-bearing analysis and curve detection) with spatial analytics to automatically identify intersections, characterize their connectivity patterns, and assign unique identifiers based on configurable parameters. When evaluated across multiple urban contexts using diverse data sources (manual digitization and OpenStreetMap), the method demonstrated consistent performance with mean Intersection over Union greater than 0.85 and F-scores more than 0.91. The high correctness and completeness metrics (both more than 0.9) confirm its ability to minimize both false positive and omission errors, even in complex roadway configurations. The approach consistently produced gap-free, overlap-free outputs, showing strength in handling interchange geometries. The solution enables transportation agencies to make data-driven maintenance decisions by providing reliable, standardized intersection inventories. Its adaptability to varying input data quality makes it particularly valuable for large-scale infrastructure monitoring and smart city applications. Full article

Collision welding represents a promising solid-state joining technique for combining both similar and dissimilar metals without the thermal degradation of mechanical properties typically associated with fusion-based methods. This makes it particularly attractive for lightweight structural applications. In the context of collision welding, it is typically assumed that ideally smooth and defect-free surface conditions exist prior to welding. However, this does not consistently reflect industrial realities, where surface imperfections such as scratches are often unavoidable. Despite this, the influence of such surface irregularities on weld integrity and quality has not been comprehensively investigated to date. In this study, collision welding is applied to the material combination of AA6110A-T6 and AA6060-T6. Initially, the process window for this material combination is determined by systematically varying the collision velocity and collision angle—the two primary process parameters—using a special model test rig. Subsequently, the effect of surface imperfections in the form of defined scratch geometries on the resulting weld quality is investigated. In addition to evaluating the welding ratio and tensile shear strength, weld quality is assessed through scanning electron microscopy (SEM) of the bonding interface and high-speed imaging of jet formation during the collision process. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article

This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between ${20}^{°}$ and ${90}^{°}$, with accuracy decreasing to 3.00% at ${10}^{°}$. The image-based angle estimator maintained a root-mean-square error $\le 3$% across most of the same range, rising to 6.4% at ${10}^{°}$. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article

This study investigates the coupled hygrothermal behavior of mycelium-based composites (MBCs) as a function of their microstructural organization, governed by fungal species, substrate type, additive incorporation, and treatment method. Eleven composite formulations were selected and characterized using a multi-scale experimental approach, combining scanning electron microscopy, dynamic vapor sorption, vapor permeability tests, capillary uptake measurements, and transient thermal conductivity analysis. SEM analysis revealed that Ganoderma lucidum forms dense and interconnected hyphal networks, whereas Trametes versicolor generates looser, localized structures. These morphological differences directly influence water vapor transport and heat conduction. Additive-enriched composites exhibited up to 21.8% higher moisture uptake at 90% RH, while straw-based composites demonstrated higher capillary uptake and free water saturation (up to 704 kg/m³), indicating enhanced moisture sensitivity. In contrast, hemp-based formulations with Ganoderma lucidum showed reduced sorption and vapor permeability due to limited pore interconnectivity. Thermal conductivity varied nonlinearly with temperature and moisture content. Fitting the experimental data with an exponential model revealed a moisture sensitivity coefficient thirty times lower for GHOP compared to VHOP, highlighting the stabilizing effect of a compact microstructure. The distinction between total and effective porosity emerged as a key factor in explaining discrepancies between apparent and functional moisture behavior. These findings demonstrate that hygric and thermal properties in MBCs are governed not by porosity alone, but by the geometry and connectivity of the internal fungal network. Optimizing these structural features enables fine control overheat and mass transfer, laying the groundwork for the development of high-performance, bio-based insulation materials. Full article

Accurate prediction of air demand in free-surface flows through high-head spillway tunnels with multiple vents represents a critical design challenge. Existing empirical formulas for estimating air demand, derived from studies of single vents using experimental and prototype data, are not directly applicable to multi-vent configurations. This study investigates the combined effects of key parameters on ventilation requirements: (1) flow characteristics (velocity range of 6–12 m/s and depth varying between 0.06 and 0.1 m); (2) vent geometry (total vent area from 28 to 140 cm² and spatial distribution). Through an experimental analysis, an empirical formula is derived to correlate wall roughness with interfacial shear stress, enabling an improved method for estimating air demand in spillway tunnels with multiple air vents. The resulting predictive model achieves ±25% agreement with two prototype case studies and model tests. These experimentally validated relationships provide quantitative guidelines for optimizing ventilation system designs. Full article

In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article

The main roadblock to precise Smartphone GNSS positioning is erroneous carrier phase data which is highly prone to cycle slips (CSs). There is a dearth of research on cycle slip detection and repair (CSDR) methods for Smartphone GNSS. Existing literature on CSDR methods is based on phase data captured by professional-grade receivers. These methods can be broadly categorized into two groups: (1) Geometry-Free CSDR (GF-CSDR) and (2) Geometry-Based CSDR (GB-CSDR). GF-CSDR methods rely on individual satellite measurements, whereas the GB-CSDR technique considers the whole satellite–receiver geometry. This paper proposes a real-time single-frequency CSDR method combining both GF and GB-CSDR. Experimental results are presented for a static and a kinematic smartphone dataset. It is shown that although reliable CS detection is possible for Smartphone GNSS, CS repair reliability is limited due to the carrier phase precision quality. Full article

To enhance the mechanics and self-healing properties of the self-healing composite, this study introduces an innovative optimization method for variable-angle fiber-reinforced self-healing composites with microtubule network carriers. The study aims to minimize macroscopic structural compliance and carrier head loss. Firstly, a topological description function (TDF) for the self-healing composite was introduced, taking into account the configuration and geometry of the macroscopic structure and microtubule network carrier as design variables. Secondly, the relationship between the fiber laying angle and component spindle direction was established. An element stiffness matrix for variable-angle fibers was derived to determine the compliance of the self-healing composite. Then, the microtubule network head loss was calculated based on the Hardy Cross method. Finally, by integrating the Moving Morphable Component (MMC) method and the enumeration method, a dual-scale collaborative optimization framework was developed. The set of double-objective Pareto non-inferior solutions of the self-healing composite was obtained by iteration. Numerical examples show that (1) under the same optimization conditions, the non-inferior solution set of variable-angle fiber design is superior to those of fixed-angle fiber designs (0°, 45°, and 90°). (2) Compared with single-objective (compliance) optimization of the carrier-free composite, the Pareto solution set of the variable-angle dual-scale collaborative optimization can provide a better compliance optimization solution, and the maximum compliance solution of the solution set is only 10.64% higher. This paper proposes a method combining variable-angle and dual-scale collaborative optimization, which provides a useful reference for the topology design of a self-healing composite. Full article

This research explores how varying proportions of virgin polyethylene terephthalate (vPET) and recycled polyethylene terephthalate (rPET) in vPET-rPET blends, combined with preform thermal conditions during the stretch blow molding (SBM) process, influence PET bottles’ microscopic characteristics. Key metrics such as viscosity, density, crystallinity, amorphous phase relaxation, and microcavitation were assessed using response surface methodology (RSM). Statistical analysis, including Analysis of variance (ANOVA) and its power, supported the interpretation of results. The first part of the work details the experimental design and statistical methods. Positron annihilation lifetime spectroscopy (PALS) and amorphous phase density analysis revealed reduced free volume size, a substantial increase in free volume quantity, and a transformation toward ellipsoidal geometries, highlighting significant structural changes in the material. At the same time, the intrinsic viscosity (IV) and PALS studies indicate that the solid-state post-condensation effect (SSPC) is linked with microcavitation through post-condensation product diffusion. The conclusions, which resulted from the microstructure analysis, affected the material’s mechanical strength and were validated by pressure resistance tests of the bottles. Full article

In this work, α-tocopherol and trolox were studied as compounds that have high biological activity. α-Tocopherol is considered a food additive because the refining process of vegetable oils causes the depletion of this vitamin, and thus, its inclusion is required to keep them from oxidizing. Computational tools have determined the antioxidant activity of these additives. The geometries of the studied molecules were optimized using two density functional methods, including M05-2X and M06-2X, in combination with the 6-311++G(2d,2p) basis set. The results indicated that when comparing the antioxidant activity of α-tocopherol and trolox, they were very similar to each other, but α-tocopherol had an antioxidant activity slightly higher, around 1.2 kJ/mol, than trolox. Thus, these additives can be used as polymer additives to protect materials from free-radical-induced stress. To test their applicability in polymeric formulations, flexible polyurethane foams were prepared with varying α-tocopherol ratios and NCO indices (1.0 and 1.1). Increasing the α-tocopherol content reduced the compressive force and altered the mechanical properties, likely due to its presence in the foam structure. This additive not only fine-tuned the mechanical properties but also provided antioxidant effects, enabling multiple enhancements in polymeric products with a single additive. Full article

Signal distortion bias (SDB) in Global Navigation Satellite System (GNSS) data processing, defined as the time difference between the distorted chip and the ideal rectangular chip, leads to systematic biases in pseudoranges, affecting satellite and receiver differential code biases (DCBs). The stability of SDBs, allowing them to be treated as constant values, highlights the importance of investigating both their stability and estimation accuracy. Two different methods are used to estimate SDBs: (1) the hybrid method and (2) the geometry-free method. Data from approximately 430 stations, spanning the entire year of 2021, were analyzed to evaluate the estimation accuracy and the short-term and long-term stability of GPS SDBs. The analysis focused on two code signals: C1C (L1 Coarse/Acquisition) and C2W (L2 P(Y)). The results show that the short-term and long-term stability of GPS C1C and C2W SDBs is comparable for both methods, with only minor variations between them. Additionally, one month of data were used to validate the accuracy of estimated SDBs across different receiver groups. The results demonstrate that geometry-free SDBs provide stable satellite DCB estimates with an average bias below 0.15 ns and minimal residual biases, while hybrid SDBs provide satellite DCB estimates with an average bias below 0.20 ns. Overall, the comparison underscores the superior performance of geometry-free SDBs in achieving consistent satellite DCB estimates. Full article