Search Results (4,174)

To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

A hybrid wave energy conversion (WEC) system, integrating a backward bent duct buoy (BBDB) with an oscillating buoy (OB) via a flexible mooring chain, is introduced in this study. Unlike existing hybrid WECs, the proposed system dispenses with rigid mechanical linkages and enables flexible offshore deployment. Flared BBDB and buoy models with spherical, cylindrical, and semi-capsule shapes are designed and tested experimentally in a wave flume using both regular and irregular wave conditions. The effects of nozzle ratio (NR), coupling distance, buoy draft, and buoy geometry are systematically examined to investigate the hydrodynamic performance and energy conversion characteristics. It is found that NR at 110 under unidirectional airflow produces an optimal balance between pressure response, free surface displacement, and energy conversion efficiency. Energy extraction is significantly influenced by the coupling distance, with the hybrid system achieving maximum performance at a specific normalized spacing. The semi-capsule buoy improves power extraction ability and expands effective bandwidth due to asymmetric shape and coupled motion. These findings provide valuable insights into the coupling mechanism and geometric optimization for hybrid WECs. Full article

The ecological rice–eel co-culture system is not only beneficial for enhancing productivity and sustainability in agriculture but also plays a crucial role in promoting environmental health. In the present study, based on the long-term positioning trial of the rice–eel co-culture system that began in 2016 and was sampled in 2023, the effects of reduced nitrogen fertilizer application on soil physico-chemical properties and the bacterial community were investigated. Treatments included a conventional regular fertilization treatment (RT), rice–eel co-culture system regular fertilization (IT), and nitrogen-reduction 10%, 30%, and 50% fertilization treatments (IT90, IT70, and IT50). Our research demonstrated the following: (1) Compared to RT, IT significantly increased soil water-stable macroaggregates (R0.25), mean weight diameter (MWD), geometric mean diameter (GMD), and available phosphorus content, with the increases of 15.66%, 25.49%, 36.00%, and 18.42%, respectively. Among the nitrogen-reduction fertilization treatments, IT90 showed the most significant effect. Compared to IT, IT90 significantly increased R0.25, MWD, GMD, and available nitrogen content, with increases of 4.4%, 7.81%, 8.82%, and 28.89%, respectively. (2) Compared to RT, at the phylum level, the diversity of Chloroflexi was significantly increased under IT and IT50, and the diversity of Gemmatimonadota was significantly increased under IT90, IT70, and IT50. The diversity of Acidobacteriota was significantly higher in IT90 and IT70 compared to IT. It was shown that the rice–eel co-culture system and nitrogen fertilizer reduction could effectively improve the degradation capacity of organic matter and promote soil nitrogen cycling. In addition, redundancy analysis (RDA) identified total phosphorus, total nitrogen, and available nitrogen (p = 0.007) as the three most important environmental factors driving changes in the bacterial community. (3) The functional prediction analysis of soil microbiota showed that, compared to RT, the diversity of pathways related to biosynthesis (carbohydrate biosynthesis and cell structure biosynthesis) and metabolism (L-glutamate and L-glutamine biosynthesis) was significantly higher under IT70, IT90, IT, and IT50 (in descending order). However, the diversity of pathways associated with degradation/utilization/assimilation (secondary metabolite degradation and amine and polyamine degradation) was significantly lower under all the rice–eel co-culture treatments. In conclusion, the rice–eel co-culture system improved soil physicochemical properties and the soil microbial environment compared with conventional planting, and the best soil improvement was achieved with 10% less N fertilizer application. Full article

To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. Full article

The method of extracting energy from a fluid environment using flapping foils offers advantages such as structural simplicity and environmental friendliness. However, its low energy harvesting efficiency remains a significant factor limiting its development. This study employs suction and blower-based control (SBC) to enhance the energy harvesting efficiency of flapping foils. Using an orthogonal experimental design and numerical methods, 49 representative combinations of SBC geometries were selected for numerical simulation. The effects and priority rankings of geometric parameters on foil performance were statistically analyzed. It was found that under the optimal geometry (the suction slot position is 0.54c, the injection slot position is 0.79c, the width of the slot is 0.015c, the angle of the suction slot is −3°, and the angle of the injection slot is −9°), the energy harvesting efficiency can reach 40.7%. Furthermore, under laminar flow conditions, the benefit of SBC increases with higher Reynolds numbers (Re). At Re = 2200, SBC maximized the improvement in energy harvesting efficiency by 76%. No significant correlation was observed between the flapping amplitude and the SBC effect. However, the reduced frequency significantly influences the efficiency improvement generated by SBC. The SBC method shifts the foil’s optimal operating region towards lower reduced frequencies, which benefits energy harvesting efficiency. The research presented herein may have potential applications in the development of marine energy systems and bio-inspired propulsion. Full article

This literature review explores the role of symmetry in thermal sciences, fluid dynamics, and energy applications, emphasizing the theoretical and practical implications. Symmetry is a fundamental tool for simplifying complex problems, enhancing computational efficiency, and improving system design across multiple engineering and physics domains. Thermal and fluid processes are applied in several modern energy use technologies, essentially involving the complex, multidimensional interaction of fluid mechanics and thermodynamics, such as renewable energy applications, combustion diagnostics, or Computational Fluid Dynamics (CFD)-based optimization, where symmetry is highly considered to simplify geometric parameters. Indeed, the interconnection between experimental analysis and the numerical simulation of processes is an important field. Symmetry operates as a unifying principle, its presence determining everything from the stability of turbulent flows to the efficiency of nuclear reactors, revealing hidden patterns that transcend scales and disciplines. Rotational invariance in pipelines; rotors of hydraulic, thermal, and wind turbines, and in many other cases, for instance, not only lowers computational cost but also guarantees that solutions validated in the laboratory can be effectively scaled up to industrial applications, demonstrating its crucial role in bridging theoretical concepts and real-world implementation. Thus, a wide range of symmetry solutions is exhibited in this research area, the results of which contribute to the development of science and fast information for decision making in industry. In this review, essential findings from prominent research were synthesized, highlighting how symmetry has been conceptualized and applied in these contexts. Full article

Electromagnetic pneumatic microvalves, widely used in knitting machines, typically operate based on a spring-return mechanism. When the coil is energized, the electromagnetic force overcomes the spring force to attract the armature, opening the valve. Upon de-energization, the armature returns to its original position under the restoring force of the spring, closing the valve. However, most existing electromagnetic microvalves adopt a radially asymmetric magnetic yoke design, which generates additional radial forces during operation, leading to armature misalignment or even sticking. Additionally, the inductance effect of the coil causes a significant delay in the armature release response, making it difficult to meet the knitting machine’s requirements for rapid response and high reliability. To address these issues, this paper proposes an improved electromagnetic microvalve design. First, the magnetic yoke structure is modified to be radially symmetric, eliminating unnecessary radial forces and preventing armature sticking during operation. Second, a permanent magnet assist mechanism is introduced at the armature release end to enhance release speed and reduce delays caused by the inductance effect. The effectiveness of the proposed design is validated through electromagnetic numerical simulations, and a multi-objective genetic algorithm is further employed to optimize the geometric dimensions of the electromagnet. The optimization results indicate that, while maintaining the fundamental power supply principle of conventional designs, the new microvalve structure achieves a pull-in time comparable to traditional designs during engagement but significantly reduces the release response time by approximately 80.2%, effectively preventing armature sticking due to radial forces. The findings of this study provide a feasible and efficient technical solution for the design of electromagnetic microvalves in textile machinery applications. Full article

Enhancing the wear resistance of structural steels used in demanding industrial applications is critical for extending components’ lifespan and ensuring mechanical reliability. In this study, we investigated the influence of flux-cored arc welding (FCAW) hardfacing on the tensile behavior of S355MC steel. Protective Fe-Cr-C alloy layers were deposited in one and two successive passes using automated FCAW, followed by tensile testing of specimens oriented at varying angles relative to the weld bead direction. The methodology integrated 3D scanning and digital image correlation to accurately capture geometric and deformation parameters. The experimental results revealed a consistent reduction in tensile strength and ductility in all the welded configurations compared to the base material. The application of the second weld layer further intensified this effect, while specimen orientation influenced the degree of mechanical degradation. Microstructural analysis confirmed carbide refinement and good adhesion, but also identified welding-induced defects and residual stresses as factors that contributed to performance loss. The findings highlight a clear trade-off between improved surface wear resistance and compromised structural properties, underscoring the importance of process optimization. Strategic selection of welding parameters and bead orientation is essential to balance functional durability with mechanical integrity in industrial applications. Full article

As a discontinuous deformation method, the block element method (BEM) characterizes a material’s elastoplastic behavior through the constitutive relation of thin-layer elements between adjacent blocks. To realistically simulate rock damage paths, this work improves the traditional BEM by using random Voronoi polygonal grids for discrete modeling. This approach mitigates the distortion of damage paths caused by regular grids through the randomness of the Voronoi grids. As the innovation of this work, the iterative algorithm is combined with polygonal geometric features so that the area–perimeter fractal dimension can be introduced to optimize random Voronoi grids. The iterative control index can effectively improve the geometric characteristics of the grid while maintaining the necessary randomness. On this basis, a constitutive relation model that considers both normal and tangential damage is proposed. The entire process from damage initiation to macroscopic fracture failure in rocks is described using two independent damage surfaces and a damage relationship based on geometric mapping relationships. The analysis results are in good agreement with existing experimental data. Furthermore, the sensitivity method is used to analyze the influence of key mechanical parameters in the constitutive model. Full article

Residential areas are characterized by closely packed buildings which disturb wind flow resulting in low wind speeds (below 2 m/s) with a high turbulence intensity (above 20%). In order to interface between off-the-shelf wind turbines and low-quality wind, the Increased velocity (INVELOX) wind delivery system is an attractive wind augmentation option for such regions. The INVELOX setup can harness more energy than conventional bare wind turbines under the same incident wind conditions. However, these systems also have drawbacks and challenges that they face in their operation, which amplify the need to review, understand, and expose gaps and flaws in pursuit of increased power production in low wind quality environments. This paper seeks to review and simplify the advances done by various scholars towards improving the INVELOX delivery system. It provides the mathematical foundation on which these advances are rooted and gives an understanding of how the improvements better the geometric properties of INVELOX. The article concludes by proposing future research directions. Full article

Landscape quality and the productivity of $\mathit{Rhododendron\; simsii}$ are directly related to the maintenance of ecological functions in the alpine region. The specific relationship between the spatial pattern of colour patches and the visual quality of R. simsii landscape recreational forests has been insufficiently explored. In this study, we constructed a model of the relationship between landscape colour patches and the aesthetic value of such a forest, analysing the key factors driving changes in its landscape quality. A total of 1549 participants were asked to assess 16 groups of landscape photographs. The results showed that variations in perceived aesthetic quality were stimulated by colour patch dynamics and spatial heterogeneity. Utilising structural equation modelling (SEM), we identified key indicators synergistically influencing aesthetic quality, including the area percentage, shape, and distribution of colour patches, which demonstrated strong explanatory power ($R^2$ = 0.83). The SEM also revealed that the red patch area, mean perimeter area ratio, and separation index are critical latent variables with standardised coefficients of 0.54, 0.65, and 0.62, respectively. These findings provide actionable design strategies: (1) optimising chromatic contrast through high-saturation patches, (2) controlling geometric complexity, and (3) improving spatial coherence. These results advance the theoretical framework for landscape aesthetic evaluation and offer practical guidance for landscape recreational forest management. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

In this paper, we develop a geometric formulation of datasets. The key novel idea is to formulate a dataset to be a fuzzy topological measure space as a global object and equip the space with an atlas of local charts using graphs of fuzzy linear logical functions. We call such a space a logifold. In applications, the charts are constructed by machine learning with neural network models. We implement the logifold formulation to find fuzzy domains of a dataset and to improve accuracy in data classification problems. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article