Search Results (4,153)

This work investigates the behavior of the coefficients of analytic functions within certain subclasses characterized by inherent symmetric structures. By leveraging deep connections with functions exhibiting positive real part properties, the approach introduces a modern analytical framework that links the studied coefficients to those of auxiliary functions with regulated behavior. This connection allows for the derivation of sharp estimates and facilitates computational treatment. The proposed method builds upon certain classical and modern coefficient inequalities. The study focuses on obtaining precise bounds for specific determinant expressions associated with initial, inverse, and inverse logarithmic coefficients, all within a subclass of starlike functions exhibiting internal symmetry aligned with a recently introduced canonical structure. This symmetric perspective reveals how geometric properties can lead to refined quantitative outcomes that enhance contemporary analytic theory. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. 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 real-space renormalization group (RG) framework is formulated for classical SO(n) spin models defined on d-dimensional crystal lattices composed of corner-sharing hyper-tetrahedra, a class of geometrically frustrated crystal structures. This includes, as specific instances, the classical Heisenberg model on the kagome and pyrochlore crystals. The approach involves computing the partition function and corresponding order parameters for spin clusters embedded in the crystal, to leading order in symmetry-breaking fields generated by surrounding spins. The crystal geometry plays a central role in determining the scaling relations and the associated critical behavior. To illustrate the efficacy of the method, a reduced manifold of symmetry-allowed ordered states for isotropic nearest-neighbor interactions is analyzed. The RG flow systematically excludes the emergence of a $\overrightarrow{q}=\mathbf{0}$ ordered phase within the antiferromagnetic sector, independently of both the spatial dimensionality of the crystal and the number of spin components. Extensions to incorporate more elaborate crystal-symmetry-induced ordering patterns and fluctuation-driven phenomena—such as order-by-disorder—are also discussed. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. 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

Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article

The paper studies the behavior of homogeneous isotropic materials by performing appropriate numerical analyses and utilizing suitable FEMs to reproduce the Brazilian splitting test. Starting with a theoretical approach and adopting suitable numerical simulations, a new formula that is able to characterize the Young’s modulus is presented. To this end, in addition to the analysis of the specimen’s response in terms of stresses and strains, the real displacement field resulting from the real kinematical constraints on the specimen is determined. Therefore, the Brazilian test is taken as a reference test and the specimen’s behavior is derived by taking advantage of both the theoretical approach and numerical simulations developed in the ANSYS 2021 R1 environment. The latter allows us to define a new mathematical relation representing the missing part of the kinematical field. Furthermore, a new formula which explicitly relates the Young’s modulus of the material to the geometrical characteristics of the specimen, to the acting force, and to a measured selected displacement is proposed. Future developments will include adopting the proposed formulas for the identification of other mechanical parameters of the material, e.g., by adopting a full-field contactless approach to displacement measurement and studying the behavior of specimens with different geometrical characteristics. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. 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

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

This study presents a cross-cultural comparison of two fortified residences—Palazzo Farnese in Italy and Dong’s Fortified Compound in China—through a triadic analytical framework encompassing architectural form, symbolic ornamentation, and public perception. By combining field observation, iconographic interpretation, and digital ethnography, the research investigates how heritage meaning is constructed, encoded, and reinterpreted across distinct sociocultural contexts. Empirical materials include architectural documentation, decorative analysis, and a curated dataset of 4947 user-generated images and 1467 textual comments collected from Chinese and international platforms between 2020 and 2024. Methods such as CLIP-based visual clustering and BERTopic-enabled sentiment modelling were applied to extract patterns of perception and symbolic emphasis. The findings reveal contrasting representational logics: Palazzo Farnese encodes dynastic authority and Renaissance cosmology through geometric order and immersive frescoes, while Dong’s Compound conveys Confucian ethics and frontier identity via nested courtyards and traditional ornamentation. Digital responses diverge accordingly: international users highlight formal aesthetics and photogenic elements; Chinese users engage with symbolic motifs, family memory, and ritual significance. This study illustrates how historically fortified residences are reinterpreted through culturally specific digital practices, offering an interdisciplinary approach that bridges architectural history, symbolic analysis, and digital heritage studies. Full article

Accurate and robust spatial perception is fundamental for dynamic 3D city modeling and urban environmental sensing. High-resolution remote sensing data, particularly LiDAR point clouds, are pivotal for these tasks due to their lighting invariance and precise geometric information. However, processing and aligning sequential LiDAR point clouds in complex urban environments presents significant challenges: traditional point-based or feature-matching methods are often sensitive to urban dynamics (e.g., moving vehicles and pedestrians) and struggle to establish reliable correspondences. While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. To address these challenges, we propose UnMinkLO-Net, an end-to-end self-supervised LiDAR odometry framework. Our method is as follows: (1) we efficiently encode 3D point cloud structures using voxel-based sparse convolution, and (2) we model inherent alignment uncertainty via covariance matrices, enabling novel self-supervised loss based on uncertainty modeling. Extensive evaluations on the KITTI urban dataset demonstrate UnMinkLO-Net’s effectiveness in achieving highly accurate point cloud registration. Our self-supervised approach, eliminating the need for manual annotations, provides a powerful foundation for processing and analyzing LiDAR data within multi-sensor urban sensing frameworks. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/$\sqrt{\mathrm{Hz}}$ @1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article