Search Results (5,314)

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness. Full article

The NNW-trending Central Mindoro Fault (CMF) is an active oblique left-lateral strike-slip fault as determined from offset morphotectonic features such as spurs and streams. Mapping of the trace and determination of the sinistral strike-slip sense of motion of the CMF is essential not only to the assessment of hazards but also to providing a clearer perspective of its role in accommodating deformation resulting from the NW relative motion between the Philippine Sea Plate and the Sunda Plate. Its sense of motion is also kinematically congruent with the NW-SE translation along a transcurrent zone between the Philippine Mobile Belt and the Palawan Microcontinental Block on the western part of the Philippine archipelago. It is also consistent with the left-lateral motion of other structures within the zone, such as the Verde Passage Fault—another structure believed to be accommodating the NW-SE translation. Mapping of the CMF provides a key constraint in identifying the possible mechanism(s) involved in the dextral strike-slip motion of the 1994 Mindoro Earthquake ground rupture, which is subparallel to the CMF. Full article

Zeta potential testing, Fourier infrared spectroscopy, and total organic carbon analysis were employed in this manuscript to explore the flocculation mechanism of polyacrylamide (PAM) on slurry with a high content of polycarboxylate ether (PCE). Through the combination of assessments of chemical bond shifts, adsorption indicators, and intrinsic viscosity of high-molecular-weight polymer systems, the microscale flocculation mechanisms of different PAM dosages in cement suspensions were elucidated, showcasing stages of “adsorption–lubrication–entanglement”. Initially (PAM < 0.3%), with PAM introduction, the polymer primarily underwent adsorption interactions, including hydrogen bonding between the ester group, amine group, and water molecules; chelation between the ester group and Ca²⁺ and Al³⁺ on the cement surface; and bridging between PAM’s long-chain structure and cement particles. As the PAM content increased, the cement particles’ adsorption capacity saturated (PAM < 0.67%). The entropy loss of polymer conformation could not be offset by adsorption energy, leading to its exclusion from the interface and depletion attractive forces. Slurry movement shifted from inter-particle motion to high-molecular-weight polymer sliding in interstitial fluid, forming a lubrication effect. With further PAM content no less than 0.67%, the polymer solution reached a critical entanglement concentration, and the contact of the rotation radius of the long-chain molecules led to entanglement domination. By introducing bridging adsorption, depletion attraction, and entanglement forces, the cohesion of cement-based polymer suspensions was subsequently determined. The results showed a linear correlation between cohesion and PAM concentration raised to powers of 0.30, 1.0, and 0.75 at different interaction stages, and a multiscale validation from microscopic flocculation mechanisms to macroscopic performance was finally completed through a comparative analysis with macroscopic anti-washout performance. Full article

The technologically advanced learning ocean system—wave energy converter (TALOS-WEC) project addresses the urgent need for sustainable and efficient energy solutions by leveraging the vast potential of wave energy. This project presents a pioneering approach to wave energy capture through its unique multi-axis and omnidirectional point absorber design. Featuring a fully enclosed power take-off (PTO) system, the TALOS-WEC harnesses energy across six degrees of freedom (DoFs) using an innovative internal reaction mass (IRM) mechanism. This configuration enables efficient energy extraction from the relative motion between the IRM and the hull, aiming for energy conversion efficiencies ranging between 75–80% under optimal conditions, while ensuring enhanced durability in harsh marine environments. The system’s adaptability is reflected in its versatile geometric configurations, including triangular, octagonal, and circular designs, customised for diverse marine conditions. Developed at Lancaster University, UK, and supported by international collaborations, the TALOS-WEC project emphasises cutting-edge advancements in hydrodynamic modelling, geometric optimisation, and control systems. Computational methodologies leverage hybrid frequency-time domain models and advanced panel codes (WAMIT, HAMS, and NEMOH) to address non-linearities in the PTO system, ensuring precise simulations and optimal performance. Structured work packages (WPs) guide the project, addressing critical aspects such as energy capture optimisation, reliability enhancement, and cost-effectiveness through innovative monitoring and control strategies. This paper provides a comprehensive overview of the TALOS-WEC, detailing its conceptual design, development, and validation. Findings demonstrate TALOS’s potential to achieve scalable, efficient, and robust wave energy conversion, contributing to the broader advancement of renewable energy technologies. The results underscore the TALOS-WEC’s role as a cutting-edge solution for harnessing oceanic energy resources, offering perspectives into its commercial viability and future scalability. Full article

In recent years, the macro–micro structure (servo platform for macro motion and galvanometer for micro motion) composed of a galvanometer and servo platform has been gradually applied to laser processing in order to address the increasing demand for high-speed, high-precision, and large-format precision machining. The research in this field has evolved from step-and-scan methods to linkage processing methods. Nevertheless, the existing linkage processing methods cannot make full use of the field-of-view (FOV) of the galvanometer. In terms of motion distribution, the existing methods are not suitable for continuous micro segments and generate the problem that the distribution parameter can only be obtained through experience or multiple experiments. In this research, a new laser linkage processing method for global trajectory smoothing of densely discretized paths is proposed. The proposed method can generate a smooth trajectory of the servo platform with bounded acceleration by the finite impulse response (FIR) filter under the global blending error constrained by the galvanometer FOV. Moreover, the trajectory of the galvanometer is generated by vector subtraction, and the motion distribution of macro–micro structure is accurately realized. Experimental verification is carried out on an experimental platform composed of a three-axis servo platform, a galvanometer, and a laser. Simulation experiment results indicate that the processing efficiency of the proposed method is improved by 79% compared with the servo platform processing only and 55% compared with the previous linkage processing method. Furthermore, the method can be successfully utilized on experimental platforms with good tracking performance. In summary, the proposed method adeptly balances efficiency and quality, rendering it particularly suitable for laser precision machining applications. Full article

The Discrete Element Method (DEM) is an innovative numerical computational approach. This method is employed to study and resolve the motion patterns of particles within discrete systems, contact mechanics properties, mechanisms of separation processes, and the relationships between contact forces and energy. Agricultural machinery involves the interactions between machinery and soil, crops, and other systems. Designing agricultural machinery can be equivalent to solving problems in discrete systems. The DEM has been widely applied in research on agricultural machinery design and mechanized harvesting of crops. It has also provided an important theoretical research approach for the design and selection of operating parameters, as well as the structural optimization of potato harvesting machinery. This review first analyzes and summarizes the current global potato industry situation, planting scale, and yield. Subsequently, it analyzes the challenges facing the development of the potato industry. The results show that breeding is the key to improving potato varieties, harvesting is the main stage where potato damage occurs, and reprocessing is the main process associated with potato waste. Second, an overview of the basic principles of DEM, contact models, and mechanical parameters is provided, along with an introduction to the simulation process using the EDEM software. Third, the application of the DEM to mechanized digging, transportation, collection, and separation of potatoes from the soil is reviewed. The accuracy of constructing potato and soil particle models and the rationality of the contact model selection are found to be the main factors affecting the results of discrete element simulations. Finally, the challenges of using the DEM for research on potato harvesting machinery are presented, and a summary and outlook for the future development of the DEM are provided. Full article

Graphite, a critical material for furnace walls, is pivotal to the reliability of the carbon-free hydrogen production industry through methane pyrolysis catalyzed by molten metals. This study systematically investigates the corrosion behavior of molten CuSn alloy on three typical commercial graphite materials—low-density graphite (LDG), high-density graphite (HDG), and pyrolytic graphite (PyG)—with a focus on their corrosion resistance and the underlying mechanisms responsible for graphite corrosion over a period of up to 1000 h at 1100 °C. The experimental results show that LDG suffered the most severe corrosion, with a mass loss of up to 60.09% and a hardness decrease from 0.73 GPa to 0.17 GPa, whereas PyG demonstrated the best corrosion resistance, with only a 5.64% mass loss and a hardness drop from 0.52 GPa to 0.35 GPa. SEM and XRD analyses revealed that the porous structures of LDG and HDG suffered significant macroscopic corrosion, caused by the stress from molten metal infiltration and aggregation in the pores, leading to structural collapse. Interestingly, all three types of graphite, including the non-porous PyG, exhibited disordered microstructural degradation as detected by Raman spectroscopy. Molecular dynamics (MD) simulations confirmed that the thermal motion of Cu and Sn atoms primarily drives the microstructural corrosion of graphite, suggesting that the corrosion process involves both micro- and macro-level damage. These findings provide crucial insight into the compatibility of different graphite materials with molten CuSn alloy and valuable guidance for material selection in methane pyrolysis devices. Full article

Optical sensors are a promising technology in structural and health monitoring due to their high sensitivity and immunity to electromagnetic interference. Because of their high sensitivity, they can register the responses of buildings to a wide range of motions, including those induced by ambient noise, or detect small structural changes caused by aging or environmental factors. In previous work, an FBG-based accelerometer was introduced that is suitable for use as an autonomous unit since it does not make use of any interrogator equipment. In this paper, we present the results of the characterization of this device, which yielded the best precision and accuracy. The results show the following: (i) improvements in the orthogonality of the sensor axes, which impact their cross-axis sensitivity; (ii) reductions in the electronic noise, which increase the signal-to-noise ratio. The results of our static characterization show that, in the worst case, we can obtain a correlation coefficient R² of 0.9999 when comparing the output voltage with the input acceleration for the X- and Y-axes of the sensor. We developed an analytical, non-iterative, 12-parameter matrix calibration approach based on the least-squares method, which allows compensation for different gains in its axes, offset, and cross-axis. To improve the accuracy of our sensor, we propose a table with correction terms that can be subtracted from the estimated acceleration. The mean error of each estimated acceleration component of the sensor is zero, with a maximum standard deviation of 0.018 m/s². The maximum RMSE for all tested positions is 6.7 × 10⁻³ m/s². Full article

Towing operations are widely applied in various fields such as maritime accident rescue, assisting large vessels entering and exiting ports, and transporting large ocean platforms. Tugboats and the towed objects form a complex multi-body system connected by flexible cables, and during operations, they are subjected to the effects of complex marine environmental loads. Current research focuses on using numerical simulations and model tests in water tanks to study the motion response of towed objects and cables under the action of environmental loads. There is a lack of research that combines the mechanical response and structural strength with the load conditions of towing operations. Taking cables as an example, most studies focus on the mechanical properties of cables without considering the impact of towing conditions. After reviewing the literature, this paper summarizes the shortcomings of the existing research and points out several potential research directions in the field of towing: the mechanical response of cables during the initial stage of towing, experiments on towing by multiple tugboats, research on composite fiber cables using experimental and finite element simulation methods, and structural optimization of components related to towing operations. Full article

This study introduces a new framework for combining calibrated mobile mapping system (MMS) data and low-cost unmanned aerial vehicle (UAV) images to generate seamless, high-fidelity 3D urban maps. This approach addresses the limitations of single-source mapping, such as occlusions in aerial top views and insufficient vertical detail in ground-level data, by utilizing the complementary strengths of the two technologies. The proposed approach combines cloth simulation filtering for ground point extraction from MMS data with deep-learning-based segmentation (U²-Net) for feature extraction from UAV images. Street-view MMS images are projected onto a top-down viewpoint using inverse perspective mapping to align diverse datasets, and precise cross-view alignment is achieved using the LightGlue technique. The spatial accuracy of the 3D model was improved by integrating the matched features as ground control points into a structure from the motion pipeline. Validation using data from the campus of Yonsei University and the nearby urban area of Yeonhui-dong yielded notable accuracy gains and a root mean square error of 0.131 m. Geospatial analysis, infrastructure monitoring, and urban planning can benefit from this flexible and scalable method, which enhances 3D urban mapping capabilities. Full article

Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (Lentinula edodes) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding R² > 0.8 and nRMSE < 0.09 for the pileus transverse and longitudinal diameters, R² = 0.53 and RMSE = 3.26 mm for the pileus height, R² = 0.79 and nRMSE = 0.12 for stipe diameter, and R² = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (R² = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives. Full article

A new coupling solution for transmitting the rotation motion between two shafts with crossed axes is proposed. Based on structural considerations, a planar (P) pair is introduced into the structure of the mechanism, presenting the advantage of reduced costs due to the constructive and manufacturing simplicity and to high reliability. The proposed mechanism is of the RRPRR type, and the structural symmetry simplifies substantially the construction of the mechanism. The constructive parameters of the mechanism are the angle and distance between the driving and driven shaft and also the length of the common normal between the axes of driving and driven revolute (R) pairs, and the axes of the revolute pairs of the coupling chain, respectively. Due to the presence of the planar pair, the Hartenberg–Denavit method of homogenous operators is not applicable. The kinematic analysis for a specified motion of the driving element requires two stages: finding the relative motions from the revolute pairs and the motions from the planar pair. The RRPRR transmission is analysed for geometrical asymmetrical and symmetrical cases; the latter is more convenient and the design principles are presented. Concerning the dimensional optimization, it is found to be a methodology for ensuring that the transmission ratio of the mechanism can be maintained within a stipulated range. Based on the kinematical calculus and geometrical optimization, the mechanism was designed and manufactured. Full article

The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications. Full article

Month-long and seasonally persistent apparent tilts in atmospheric radar scatterers have been measured with a network of six windprofiler radars over periods of two or more years. The method used employs cross-correlations between vertical winds and horizontal winds measured using the radars. It is shown that large-scale apparent tilts that persisted for many weeks and months were not uncommon at many sites, with typical tilts varying from horizontal to ~3–4° from horizontal. The azimuthal and zenithal alignment of the tilts depend on local orography as well as local seasonal atmospheric conditions. It is demonstrated that these apparent tilts are not, in general, true large-scale phenomena, but rather are a manifestation of coordinated motions within turbulent and quasi-specular radar-scattering structures at scales between a few metres and tens of metres, with these structures themselves being defined by larger-scale and longer-term physical processes. Windshear combined with breaking gravity waves seems to be a particularly effective mechanism for producing these tilts, although other possibilities are also discussed. Implications for the interpretation of the nature of turbulent eddies, the accuracy of vertical wind measurements, and the nature of layering and scattering in the real atmosphere, are discussed. A method which allows for accurate measurements of the mean off-horizontal alignment of anisotropic scatterers and turbulent eddies is introduced. Full article

This paper presents an original and comprehensive comparative analysis of eight fractal analysis methods, including Box Counting, Compass, Detrended Fluctuation Analysis, Dynamical Fractal Approach, Hurst, Mass, Modified Mass, and Persistence. These methods are applied to artificially generated fractal data, such as Weierstrass–Mandelbrot functions and fractal Brownian motion, as well as natural datasets related to environmental and geophysical domains. The objectives of this research are to evaluate the methods’ capabilities in capturing fractal properties, their computational efficiency, and their sensitivity to data fluctuations. Main findings indicate that the Dynamical Fractal Approach consistently demonstrated the highest accuracy across different datasets, particularly for artificial data. Conversely, methods like Mass and Modified Mass showed limitations in complex fractal structures. For natural datasets, including meteorological and geological data, the fractal dimensions varied significantly across methods, reflecting their differing sensitivities to structural complexities. Computational efficiency analysis revealed that methods with linear or logarithmic complexity, such as Persistence and Compass, are most suited for larger datasets, while methods like DFA and Dynamic Fractal Approaches required higher computational resources. This study provides an original comparative study for researchers to select appropriate fractal analysis techniques based on dataset characteristics and computational limitations. Full article