Search Results (3,402)

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

Three-dimensional (3D) reconstruction using images is one of the most significant topics in computer vision and photogrammetry, with wide-ranging applications in robotics, augmented reality, and mapping. This study investigates methods of 3D reconstruction using video (especially monocular video) data and focuses on techniques such as Structure from Motion (SfM), Multi-View Stereo (MVS), Visual Simultaneous Localization and Mapping (V-SLAM), and videogrammetry. Based on a statistical analysis of SCOPUS records, these methods collectively account for approximately 6863 journal publications up to the end of 2024. Among these, about 80 studies are analyzed in greater detail to identify trends and advancements in the field. The study also shows that the use of video data for real-time 3D reconstruction is commonly addressed through two main approaches: photogrammetry-based methods, which rely on precise geometric principles and offer high accuracy at the cost of greater computational demand; and V-SLAM methods, which emphasize real-time processing and provide higher speed. Furthermore, the application of IMU data and other indicators, such as color quality and keypoint detection, for selecting suitable frames for 3D reconstruction is investigated. Overall, this study compiles and categorizes video-based reconstruction methods, emphasizing the critical step of keyframe extraction. By summarizing and illustrating the general approaches, the study aims to clarify and facilitate the entry path for researchers interested in this area. Finally, the paper offers targeted recommendations for improving keyframe extraction methods to enhance the accuracy and efficiency of real-time video-based 3D reconstruction, while also outlining future research directions in addressing challenges like dynamic scenes, reducing computational costs, and integrating advanced learning-based techniques. Full article

To address the challenges commonly encountered in civil aircraft operating under multi-mode, strongly coupled closed-loop control—namely scarce fault samples, pronounced distribution shift, and root-cause explanations that are easily confounded by covariates—this paper proposes a root-cause analysis method that integrates ensemble learning with constraint-guided reasoning by large language models (LLMs). First, for Full Authority Digital Engine Control (FADEC) monitoring sequences, a feature system comprising environment-normalized ratios, mechanism-informed mixing indices, and multi-scale temporal statistics is constructed, thereby improving cross-mode comparability and enhancing engineering-semantic expressiveness. Second, in the anomaly detection stage, a cost-sensitive LightGBM model is adopted and a validation-set-based adaptive thresholding strategy is introduced to achieve robust identification under highly imbalanced fault conditions. Furthermore, for Root Cause Analysis (RCA), a “computation–reasoning decoupling” framework is developed: Shapley Additive exPlanations (SHAP) are used to generate segment-level contribution evidence, while causal chains, engineering prohibitions, and structured output templates are injected into prompts to constrain the LLM, enabling it to infer root-cause candidates and produce structured explanations under mechanism-consistency constraints. Experiments on real flight data demonstrate that our method yields an anomaly detection F1-score of 0.9577 and improves overall RCA accuracy to 97.1% (versus 62.3% for a pure SHAP baseline). Practically, by translating complex high-dimensional data into actionable natural language diagnostic reports, the proposed method provides reliable and interpretable decision support for rapid RCA. Full article

Global agricultural production is severely threatened by the intensification of crop diseases and pests. Traditional pesticide application methods, characterized by inefficiency and frequent phytotoxicity, necessitate the urgent development of smart plant protection technologies that feature precision, dosage reduction, and high efficiency. This study focuses on the core component of intelligent decision-making, systematically delineating the technological trajectory of the field through a three-tier analytical framework: “model evolution–system integration–application form.” Analysis reveals that decision-making models have transitioned from rule-driven and data-driven approaches to fusion-driven paradigms. This evolution marks a shift from the codification of empirical experience to data learning, culminating in the synergistic integration of multi-source information and domain knowledge. At the system application level, the core technical architecture—comprising multi-dimensional information sensing, real-time edge computing, and precise control execution—has facilitated the translation of intelligent pesticide application from laboratory settings to field deployment. Future decision-making systems are projected to evolve towards causal understanding, cluster collaboration, and ubiquitous service, providing critical technical support for the green transformation and sustainable development of agriculture. Full article

This paper proposes a simplified mechanical model to estimate transverse shrinkage and angular distortion in multi-pass butt welding. The simplified mechanical model is first derived for an I-groove joint by representing the heated weld region with one-dimensional bar elements and by enforcing force equilibrium to obtain closed-form expressions for pass-by-pass deformation increments and cumulative deformation. For non-I-groove joints, the same simplified mechanical model is applied by updating the layer partition and geometric parameters for each pass based on the pass-wise high-temperature region; the inherent shrinkage of each pass is evaluated from the heat input and an equivalent heated-layer thickness. The simplified mechanical model is validated for V-groove multi-pass joints by comparison with thermo-elastic-plastic finite element (FE) analyses and available experimental data, and for X-groove multi-pass joints by comparison with thermo-elastic-plastic FE analyses. In addition, a parametric study on the V-groove angle (40°–70°) for SUS316L demonstrates that the model captures the increasing trend of final transverse shrinkage with groove angle without a pronounced degradation in prediction accuracy. The results show that the simplified mechanical model reproduces both deformation histories and final values with good accuracy while using only a small set of input parameters and negligible computational cost, making it useful for early-stage welding procedure planning and quick parameter studies. Full article

This study presents a two-dimensional computational fluid dynamics analysis of a vertical-axis Savonius-type wind turbine under atmospheric conditions representative of an educational environment located in the Ecuadorian Andean region. Unlike previous studies conducted under sea-level meteorological conditions, this research is performed under high-altitude conditions (2723 m a.s.l.). The unsteady flow around the rotor was simulated using a two-dimensional approach based on the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, discretized with the finite volume method and coupled with the k–ω Shear Stress Transport (SST) turbulence model. The rotor rotation was modeled using sliding mesh technique, employing a second-order implicit time scheme to ensure numerical stability and adequate temporal resolution. The numerical model was configured for a tip speed ratio of 0.8 and a wind speed of 3.9 m/s. The time step was defined based on a constant angular advancement of the rotor per time iteration, ensuring numerical stability and adequate temporal resolution. The aerodynamic torque was obtained by integrating the pressure and viscous forces acting on the blades, allowing the calculation of the mechanical power generated and the power coefficient. The results showed a periodic and stable torque behavior after the initial transient cycles, yielding an average torque of 0.7687 N·m and a mechanical power of 5.17 W, while the power coefficient reached a value of 0.2102. Analysis of the flow fields revealed the formation of a low-velocity wake downstream of the rotor, regions of high turbulent kinetic energy associated with periodic vortex shedding, and a significant pressure difference between the advancing and returning blades, confirming that turbine operation is dominated by drag forces. The numerical results were validated through comparison with previous studies, showing good agreement and demonstrating the reliability of the proposed Computational Fluid Dynamics (CFD) approach. This study highlights the potential of Savonius turbines for low-power applications in urban and educational environments, as well as the usefulness of CFD as a tool for evaluating and optimizing their aerodynamic performance. Full article

Efficient ortho–para hydrogen conversion is essential to suppress spontaneous heat release and boil-off losses during cryogenic liquid hydrogen storage and pre-liquefaction processes. In this study, a novel catalyst-filled wavy plate-fin heat exchanger (CFHE) is proposed to simultaneously enhance heat transfer and ortho–para hydrogen conversion under cryogenic conditions. Compared with conventional straight-fin configurations, the wavy-fin structure introduces controlled flow perturbations and increased specific surface area, thereby intensifying transport processes. Three-dimensional computational fluid dynamics (CFD) simulations, using the SST k–ω turbulence model, coupled with an ortho–para hydrogen conversion kinetic model were performed to quantitatively investigate the effects of key geometric parameters and catalyst loading on hydrogen conversion, heat transfer, and pressure drop within a Reynolds number range of 941–1577 and a temperature range of 35–20 K. Within the same CFHE configuration, the para-hydrogen fraction remains nearly unchanged without catalyst but increases significantly with catalyst loading. However, the catalyst reduces the global average Colburn j-factor by about 25%. Despite higher friction losses, the outlet–inlet temperature difference decreases to about 0.866 times that of the non-catalyst case, indicating improved temperature uniformity. A comprehensive performance index e, integrating heat transfer enhancement, flow resistance, and conversion efficiency, was introduced and optimized using a genetic algorithm. The optimized CFHE achieves an outlet para-hydrogen fraction exceeding 95% of the thermodynamic equilibrium value while maintaining hydrogen entirely in the gaseous phase to avoid catalyst deactivation. Overall, the catalyst-packed wavy channel configuration demonstrates superior conversion efficiency, enhanced thermal uniformity, and improved overall performance compared with straight-fin structures, providing quantitative design guidance for high-performance heat exchangers in cryogenic hydrogen liquefaction systems. Full article

Additive manufacturing (AM) of Ti-6Al-4V alloy is widely used in aerospace and medical fields due to its excellent strength and corrosion resistance. However, the microstructural heterogeneity induced by the AM process often results in fatigue properties inferior to those of their forged counterparts. Synchrotron Radiation Computed Tomography (SR-CT) was employed to conduct an in situ three-dimensional investigation of fatigue damage evolution in Ti-6Al-4V alloy fabricated via laser powder bed fusion (LPBF). Experimental results revealed phenomena of crack bridging and deflection, accompanied by the consistent presence of local high-density zones (LHDZs) throughout the fatigue damage progression. Combined with quantitative analysis of crack propagation rates, the influence of LHDZs on fatigue damage evolution was analyzed, and the relationship between AM processes, LHDZs, and fatigue damage was discussed. The results indicate that the basket-weave α-phase microstructure in Ti-6Al-4V prepared by LPBF exhibits a high correlation with the distribution of LHDZs, and the orientation of LHDZs aligns with the crack propagation direction. By adjusting process parameters such as cooling rate and temperature gradient, the formation of LHDZs can be modified, thereby influencing the fatigue properties of the material. This provides theoretical support for achieving process optimization of the fatigue properties of Ti-6Al-4V alloy prepared via LPBF. Full article

Finite-difference approaches are widely employed to solve partial differential equations in numerous practical applications. However, their computational efficiency is often limited by the need to solve linear systems through matrix inversion or iterative solvers, a challenge that is particularly acute in high-dimensional problems. Consequently, there is a growing demand for methods that ensure both high accuracy and computational efficiency. To address the two-dimensional (2D) heat conduction problem, we propose a novel hybrid technique that integrates a fourth-order implicit compact finite-difference approach with the discrete sine transform (DST). The incorporation of the DST significantly reduces the computational burden associated with solving the heat conduction equation on large grids. Detailed numerical experiments were conducted to evaluate this solver for 2D heat conduction equations subject to homogeneous Dirichlet boundary conditions (DBCs). The results demonstrate that the proposed method not only achieves substantial reductions in computational cost but also maintains a high level of numerical accuracy. All numerical experiments were performed on a computer running MATLAB R2024b. Full article

The load transfer method is important for the settlement prediction of axially loaded piles, but in multi-layered complex soils, it lacks analytical solutions. Traditional numerical methods such as the finite element method suffer from strong dependence on mesh generation, time-consuming iterative calculations, and high computational costs for back-analysis. This paper proposes a load transfer analysis model based on a Domain Decomposition Physics-Informed Neural Network. A multi-subnet parallel architecture is adopted to simulate multi-layered soils, solving the problem of inter-layer stress–strain discontinuity through interface coupling and gradient continuity constraints; a non-dimensionalization system and a hard constraint mechanism are introduced to enhance training efficiency and physical consistency; and a two-stage analysis framework comprising surrogate model forward analysis and field data inversion is established. Numerical experimental results indicate that the forward analysis of this model is in high agreement with FEM simulation results, and computational efficiency is improved by six orders of magnitude; based on a small amount of field static load test data, multi-layer soil parameters are accurately inverted, achieving more precise pile settlement prediction than FEM. Comparative analysis validates the effectiveness of the domain decomposition multi-subnet over a single network, demonstrating extensibility to hyperbolic and exponential multi-soil constitutive models. Full article

Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework. Full article

This paper investigates the finite-horizon survival probability for a system of correlated arithmetic Brownian motions with heterogeneous drifts and volatilities, focusing on the event in which one component remains strictly below all others. Using a whitening transformation of the covariance structure, we reduce the problem to the survival of a standard Brownian motion in a simplicial cone, characterized by its spherical cross-section. While explicit solutions are available in low dimensions, we address the computationally challenging tetrahedral angular case. We derive a semi-analytic formula for the survival probability via an eigenfunction expansion of the Dirichlet Laplace–Beltrami operator on this curved domain. For efficient implementation, we construct a diffeomorphism from the spherical tetrahedron to a fixed Euclidean tetrahedron, enabling the computation of angular eigenpairs through a stable finite-element scheme. For higher-dimensional regimes, we also introduce a covariance-based difficulty index and geometric bounds based on an inscribed spherical cap to assess spectral convergence and estimate long-time decay rates. Numerical experiments show that this offline–online approach achieves high accuracy and substantial speedups relative to Monte Carlo benchmarks. Full article

Orthogonal Linear Discriminant Analysis (OLDA) has been widely studied for dimensionality reduction, as the orthogonality constraints on its projection matrix enable more effective elimination of redundant features compared to conventional Linear Discriminant Analysis (LDA). However, most existing methods for solving OLDA rely on iterative optimization to sequentially construct orthogonal components, incurring massive repeated high-cost matrix operations and thus leading to substantial computational inefficiency. To address this limitation, we propose QR decomposition-based Fast OLDA (QR-FOLDA), a method built upon a theoretical result (Theorem 1) established in this work: the optimal solutions of LDA remain valid under any full-rank linear transformation. By leveraging this property, QR-FOLDA applies QR decomposition directly to the optimal LDA solution, thereby enforcing orthogonality while avoiding the repeated high-cost matrix operations typically involved in iterative optimization procedures. Experimental evaluations conducted on nine real-world datasets across various domains show that QR-FOLDA not only achieves substantial improvements in computational efficiency compared to existing OLDA methods but also delivers superior classification performance. These findings position QR-FOLDA as a theoretically sound and practically efficient solution for orthogonal discriminant analysis. Full article

The Particle swarm optimization (PSO) algorithm has strong universality and fast convergence speed, but when solving complex multimodal optimization problems, it is prone to fall into local optimum due to insufficient population diversity. To address this issue, this paper proposes a dual-population hybrid particle swarm optimization algorithm based on Hooke’s law competition mechanism (HLCM-DHPSO). This algorithm integrates the differential evolution algorithm into the PSO framework, and the two subpopulation sizes dynamically compete for computing resources according to the adaptive mechanism of Hooke’s law. When the algorithm stagnates, HLCM-DHPSO can automatically trace back to historical archives and adjust the inertia weight based on excellent experience data. Meanwhile, HLCM-DHPSO adaptively adjusts the acceleration coefficient through the Sine function to enhance the algorithm’s ability to escape from local optimum. To verify the effectiveness of the HLCM-DHPSO algorithm, it is compared with eight advanced optimization algorithms on the CEC2017 benchmark test set. The experimental results show that HLCM-DHPSO significantly outperforms the comparison algorithms in terms of solution performance, especially in handling high-dimensional and multi-peak complex functions, demonstrating superior global search and optimization capabilities. Full article

This study presents the development of a three-dimensional (3D) filament assembly model for predicting the air permeability of woven fabrics composed of spun yarns. To address the limitations of conventional single-line yarn models, the proposed framework incorporates fiber-level geometric representations using non-uniform rational B-splines (NURBS) and simulates multiple weave patterns—including plain, basket, twill, and rib—under various set density configurations. Each yarn was modeled with accurate filament distribution and cross-sectional layering, enabling the construction of realistic unit-cell-based CAD geometries. Computational fluid dynamics (CFD) simulations were performed using the k-ε turbulence model in SolidWorks Flow Simulation and validated against experimental measurements conducted under ISO 9237:1995 conditions. The filament assembly model achieved high predictive accuracy, exhibiting a lower of percentage prediction errors than the single-line yarn path model, thereby more effectively capturing airflow behavior through inter-yarn and intra-yarn pores. These findings highlight the capability of integrated CAD/CFD methodologies for virtual prototyping of breathable textiles and provide a robust foundation for high-precision performance prediction in functional and technical fabric design. Full article