Search Results (9,195)

The fully tailless configuration has lower observability, less structural weight and less drag, and it is considered one of the preferred designs for the next generation of efficient supersonic combat aircraft. In the conceptual design of such novel aircraft, a parametric geometry model is essential for multidisciplinary design analysis and optimization (MDAO). This paper presents a parametric three-dimensional (3D) geometry modeling methodology and tool for MDAO in the conceptual design of a notional supersonic tailless combat aircraft (STCA). The geometries of the STCA components (wing, fuselage and propulsion) are defined specifically by a set of parameters. In particular, the inlet and nozzle geometries are defined with the required details. Based on the geometric relationships among the STCA components, an approach involving master-dependent parameters is proposed. The geometry model generated by the approach has features such as the fuselage being blended smoothly with the wing and the propulsion being well integrated with the fuselage. Moreover, the geometry model can be generated by simply specifying the values of the master parameters, and the number of parameters required to generate the geometry model is reduced substantially. Based on the methodology, a parametric geometry modeling tool for the STCA conceptual design is developed using a Visual Basic (VB) script in the CATIA V5 platform. The applicability of the tool is validated with several case studies. Full article

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in a query image and their boundaries may overlap or be partially occluded. We present Render-Rank-Refine, a two-stage framework operating on coarse semantic meshes without requiring textured models or per-scene fine-tuning. First, panoramas rendered from the mesh enable global retrieval of coarse pose hypotheses. Then, perspective views from the top-k candidates are compared to the query via rotation-invariant circular descriptors, which re-ranks the matches before final translation and rotation refinement. Our method increases camera localization accuracy compared to the state-of-the-art SPVLoc baseline by reducing the translation error by 40.4% and the rotation error by 29.7% in ambiguous layouts, as evaluated on the Zillow Indoor Dataset. In terms of inference throughput, our method achieves 25.8–26.4 QPS, (Queries Per Second) which is significantly faster than other recent comparable methods, while maintaining accuracy comparable to or better than the SPVLoc baseline. These results demonstrate robust, near-real-time indoor localization that overcomes structural ambiguities and heavy geometric assumptions. Full article

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

Algorithmic trading heavily relies on the optimization of rule-based strategies to maximize profitability and ensure robustness under volatile market conditions. Traditional optimization methods often face limitations when dealing with the nonlinear, high-dimensional, and dynamic nature of financial search spaces. This study introduces a Metaheuristic-based framework for financial strategy optimization that focuses on the modeling and resolution of the problem through population-based search algorithms. The framework evaluates four Metaheuristic optimization techniques within a unified design, enabling a consistent and fair comparison of their performance in optimizing trading rules. To ensure realistic and time-consistent evaluation, the experimental setup incorporates a Rolling Windows Validation approach, allowing the assessment of model performance across successive market periods. Beyond improving convergence behavior, Diversity is employed as a metric to assess the quality and exploration capability of the search process, providing deeper insight into algorithmic performance. Experimental results, obtained from real market data, demonstrate substantial improvements in profitability consistency and risk-adjusted performance compared to conventional optimization approaches. The findings confirm that Metaheuristic optimization offers a robust and flexible alternative for the design and refinement of algorithmic trading systems in complex and dynamic financial environments. Interestingly, Differential Evolution exhibited persistently high Diversity, suggesting the presence of multiple distant yet competitive optima in the financial search space, where functional convergence coexists with geometric dispersion. Full article

A Hermite-based framework for reliability assessment within the limit state method is developed in this paper. Closed-form design quantiles under a four-moment Hermite density are derived by inserting the Gaussian design quantile into a calibrated cubic translation. Admissibility and implementation criteria are established, including a monotonicity bound, a positivity condition for the platykurtic branch, and a balanced Jacobian condition for the leptokurtic branch. Material data for the yield strength and ductility of structural steel are fitted using moment-matched Hermite models and validated through goodness-of-fit tests. A truss structure is subsequently analysed to quantify how non-Gaussian input geometry influences structural resistance and its associated design value. Variance-based Sobol sensitivity analysis shows that departures of the radius distribution toward negative skewness and higher kurtosis increase the first-order contribution of geometric variables and thicken the lower tail of the resistance distribution. The closed-form Hermite design resistances agree closely with numerical integration results and reveal systematic deviations from FORM estimates, which depend solely on the mean and standard deviation. Monte Carlo simulations confirm these trends and highlight the slow convergence of tail quantiles and higher-order moments. The proposed approach remains fully compatible in the Gaussian limit and offers a practical complement to EN 1990 verification procedures when skewness and kurtosis have a significant influence on design quantiles. Full article

China is the world’s third-largest sugarcane producer. When mechanically harvested sugarcane enters the sugar mill, impurity rate detection is required. However, due to the piling up of sugarcane, significant errors may occur in the detection results. Therefore, this research addresses the issue of low accuracy in machine vision detection due to the dense stacking of sugarcane. An innovative graded device was developed, featuring a three-stage progressive geometric constraint system with roller-belt gaps of 100 mm, 45 mm, and 33 mm, alongside differential traction with speed ratios of 3:1, 4:1, and 5:1. Utilizing the normal distribution characteristic for the diameter of 500 sugarcane stalks, the gap parameters were refined through a dynamic stiffness model. Through power validation and multi-factor orthogonal experiments, the study uncovered the interactive influences of sugarcane weight, primary conveyor belt speed, and speed ratio on the single-layer rate and area ratio. Our findings indicate that sugarcane weight is the primary determinant of the material’s single-layer rate, while the speed ratio is crucial for managing sugarcane distribution density, more so than the primary conveyor belt speed. Notably, increasing the speed ratio from 3:1 to 5:1 results in a decrease in area ratio from 26.8% to 22.0%. After utilizing the graded differential device, the average accuracy of machine vision detection achieved 94.90%, with only two misidentifications on average. In comparison to the control group, detection accuracy improved by 26.93%, misidentifications dropped by about 81.80%, and detection speed was recorded at 55.5 ms. These outcomes confirm that the device not only enhances detection accuracy but also significantly lowers the misidentification rate, thereby creating a stable, clear, and efficient detection environment. Full article

Despite advancements in deep learning-based Monocular Depth Estimation (MDE), applying these models to video sequences remains challenging due to geometric ambiguities in texture-less regions and temporal instability caused by independent per-frame inference. To address these limitations, we propose STF-Depth, a novel post-processing framework that enhances depth quality by logically fusing heterogeneous information—geometric, semantic, and panoptic—without requiring additional retraining. Our approach introduces a robust RANSAC-based Vanishing Point Estimation to guide Dynamic Depth Gradient Correction for background separation, alongside Adaptive Instance Re-ordering to clarify occlusion relationships. Experimental results on the KITTI, NYU Depth V2, and TartanAir datasets demonstrate that STF-Depth functions as a universal plug-and-play module. Notably, it achieved a 25.7% reduction in Absolute Relative error (AbsRel) and significantly enhanced temporal consistency compared to state-of-the-art backbone models. These findings confirm the framework’s practicality for real-world applications requiring geometric precision and video stability, such as autonomous driving, robotics, and augmented reality (AR). Full article

The purpose of this study is to investigate the buckling behavior and failure mechanism of the boom of large-scale elevating jet fire trucks, so as to provide support for its safety design and service life improvement. In terms of research methods, a combination of double-version control tests and refined finite element simulations was adopted to carry out a systematic study. The research results show that the boom base plate exhibits typical sinusoidal wave buckling deformation when the load coefficient is between 0.45 and 0.5, and the wavelength is highly consistent with the theoretical prediction; under the critical load, the strain amplitude shows a significant nonlinear jump, which confirms the buckling mechanism of the coupling between geometric nonlinearity and material plasticity; under the ultimate load, the structure undergoes local buckling failure, the failure location is in good agreement with the simulation prediction, and the test results are highly consistent with the simulation results within the engineering allowable range, which verifies the reliability and applicability of the model. The research conclusion is the establishment of evaluation criteria for buckling failure of box-type knuckle arms: visible buckling waves appear, and the strain exceeds 40%. Based on this conclusion, optimizing the width-thickness ratio of the plate, strengthening the web constraint and improving the manufacturing process can effectively enhance the anti-buckling performance of the thin-walled box structure. Full article

The integration of an inertial navigation system (INS) with the Global Navigation Satellite System (GNSS) is crucial for suppressing the error drift of the INS. However, traditional fusion methods based on the extended Kalman filter (EKF) suffer from geometric inconsistency, leading to biased estimates—a problem markedly exacerbated under large initial misalignment angles. The invariant extended Kalman filter (IEKF) embeds the state in the Lie group SE₂(3) to establish a more consistent framework, yet two limitations remain. First, its standard update fails to synergize complementary error information within the left-invariant formulation, capping estimation accuracy. Second, velocity and position states converge slowly under extreme misalignment. To address these issues, a multiplicative federated IEKF (MF-IEKF) was proposed. A geometrically consistent state propagation model on SE₂(3) is derived from multiplicative IMU pre-integration. Two parallel, mutually inverse left-invariant error sub-filters (ML₁-IEKF and ML₂-IEKF) cooperate to improve overall accuracy. For large-misalignment scenarios, a short-term multiplicative right-invariant sub-filter is introduced to suppress initial position and velocity errors. Extensive Monte Carlo simulations and KITTI dataset experiments show that MF-IEKF achieves higher navigation accuracy and robustness than ML₁-IEKF. Full article

Extrusion of AlZnMgCu alloys is associated with a very high plastic resistance of the materials at forming temperatures and significant friction resistance, particularly at the contact surface between the ingots and the container. In technological practice, this translates into high maximum extrusion forces, often close to the capacity of hydraulic presses, and the occurrence of surface cracking of extruded profiles, resulting in a reduction in metal exit speed (production process efficiency). The accuracy of mathematical material models describing changes in the plastic stress of a material as a function of deformation, depending on the forming temperature and deformation speed, plays a very important role in the numerical modelling of extrusion processes using the finite element method (FEM). Therefore, three mathematical material models of the tested aluminium alloy were analysed in this study. In order to use the results of plastometric tests determined on the Gleeble device, they were approximated with varying degrees of accuracy using the Hnsel–Spittel equation and then implemented into the material database of the QForm-Extrusion^® programme. A series of numerical FEM calculations were performed for the extrusion of Ø50 × 3 mm tubes made of 7075 aluminium alloy using chamber dies for two different billet heating temperatures, 480 °C and 510 °C, and for three different material models. The metal flow was analysed in terms of geometric stability and dimensional deviations in the wall thickness of the extruded tube and its surface quality, as well as the maximum force in the extrusion process. Experimental studies of the industrial extrusion process of the tubes, using a press with a maximum force of 28 MN and a container diameter of 7 inches, confirmed the significant impact of the accuracy of the material model used on the results of the FEM numerical calculations. It was found that the developed material model of aluminium alloy 7075 number 1 allows for the most accurate representation of the actual conditions of deformation and quality of extruded tubes. Moreover, the material data obtained on the Gleeble simulator made it possible to determine the limit temperature of the extruded alloy, above which the material loses its cohesion and cracks appear on the surface of the extruded profiles. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets of the sphere, emphasizing both continuous and discrete settings. We first present a detailed geometric and analytical foundation for intrinsic quantization on the unit sphere, including definitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation, the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantization dimensions. Building upon this framework, we develop explicit continuous and discrete quantization models on spherical curves, namely great circles, small circles, and great circular arcs— supported by rigorous derivations and pedagogical exposition. For uniform continuous distributions, we compute optimal sets of n-means and the associated quantization errors on these curves; for discrete distributions, we analyze antipodal, equatorial, tetrahedral, and finite uniform configurations, illustrating convergence to the continuous model. The central conclusion is that for a uniform probability distribution supported on a one-dimensional geodesic subset of total length L, the optimal n-means form a uniform partition and the quantization error satisfies Vn = L²/(12n²). The exposition emphasizes geometric intuition, detailed derivations, and clear step-by-step reasoning, making it accessible to beginning graduate students and researchers entering the study of quantization on manifolds. This article is intended as an expository and tutorial contribution, with the main emphasis on geometric reformulation and pedagogical clarity of intrinsic quantization on spherical curves, rather than on the development of new asymptotic quantization theory. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

High-quality hole machining of Ti-6Al-4V is critical for precision aerospace components but remains challenging due to the alloy’s poor machinability. In this study, the influence of cutting parameters on milling force, burr formation and the hole quality of Ti-6Al-4V was investigated. The mechanical properties and microstructure of the milled holes were analyzed. The research results show that milling depth is the primary factor governing variations in milling force and burr formation. The minimum milling force of 3.61 N is achieved at a milling depth of 60 μm, a feed per tooth of 2 μm/z and a cutting speed of 31 m/min. Compared to pre-optimization parameters, the milling force is decreased by 91.74%. Correspondingly, entrance burr width and hole-axis deviation were substantially reduced, indicating marked improvement in hole quality and geometrical accuracy. Microstructural observations show no deleterious phase transformations or excessive work-hardening under the optimized regime. The results deliver quantitative guidelines for parameter selection and tool application in micro-hole milling of Ti-6Al-4V and provide a foundation for further process modelling and optimization for aerospace manufacturing. Full article

A reliable and data-based teaching quality evaluation is essential for the continuous improvement of higher-education systems. However, the inherent ambiguity of assessment indicators and the subjectivity of evaluators render traditional, crisp-value models insufficient. To address this challenge, we develop a novel intuitionistic fuzzy multi-attribute decision-making framework that integrates Yager triangular norms (t-norms) with the geometric Heronian mean. Specifically, we first introduce intuitionistic fuzzy operations based on Yager t-norms and Yager t-conorms and subsequently construct two aggregation operators: the intuitionistic fuzzy geometric Heronian mean operators and the intuitionistic fuzzy weighted geometric Heronian mean operators. The idempotency, monotonicity, and boundedness properties of these operators are formally proven. Next, the intuitionistic fuzzy weighted geometric Heronian mean operators are employed to develop an approach for multi-attribute decision-making in classroom teaching quality evaluation under intuitionistic fuzzy information. Moreover, an application case study of teaching quality evaluation in an intuitionistic fuzzy environment is presented to demonstrate the practicality and effectiveness of the proposed approach. Additionally, sensitivity and comparative analyses with other techniques are carried out to further confirm the coherence and superiority of the recommended approach. The research results clearly show that our proposed method is highly effective in accurately evaluating teaching quality and can serve as a valuable tool for educational institutions in enhancing their teaching quality management. Full article