Search Results (324)

Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and unstable convergence in factorial-scale search spaces. This paper proposes an adaptive NSGA-II (A-NSGA-II) framework with coordinated enhancements at the evaluation, modelling, and algorithmic levels. A three-objective system that minimises split-block count, topological-spatial discrepancy, and layout fragmentation is established, together with an aggregate evaluation score (AES) for engineering decision-making; nodal pressure energies are reconstructed via the longest path on a directed acyclic graph; and topology-aware initialisation, Lagrange three-point interpolated adaptive operators, and periodic memetic local search are integrated within NSGA-II. Experiments on two mine ventilation networks (75 and 112 branches) over 30 independent trials show that A-NSGA-II consistently outperforms four benchmarks (NSGA-II, MOEA/D, SPEA2, and MOSA) in terms of split-block count, AES, and hypervolume; statistical tests confirm significant, large-effect HV advantages on the 112-branch network, while the 75-branch network shows a 56.6–71.5% reduction in HV standard deviation. Full article

Accurate characterization of underground geological conditions is essential for gas control, geological hazard assessment, and safe coal mining operations. However, conventional geological interpretation methods often suffer from limited spatial accuracy due to borehole deviation, sparse geological control, and insufficient integration of multi-source borehole data. To address these limitations, this study proposes an integrated geological characterization framework combining resistivity-based image logging, borehole trajectory correction, and BIM-based three-dimensional geological modeling using 135 gas extraction boreholes from the Coal Seam 15-21050 working face of Pingdingshan No. 8 Coal Mine, China. Multi-parameter logging data, including natural gamma, apparent resistivity, natural potential, and borehole image observations, were used to identify coal seam lithology, stratigraphic interfaces, and structural characteristics. Borehole trajectory analysis revealed systematic deviation patterns controlled by borehole inclination, lithological heterogeneity, and drilling conditions, highlighting the necessity of trajectory correction for accurate spatial positioning. Trajectory-corrected borehole coordinates were subsequently integrated into a BIM-based three-dimensional geological reconstruction workflow using spatial interpolation methods. The resulting model successfully reproduced coal seam geometry, interburden distribution, and localized concealed structural anomalies. Coal Seam 15 exhibited thicknesses ranging from 2.69 to 3.47 m, while Coal Seam 16–17 ranged from 1.51 to 2.38 m. The proposed workflow improved the reliability of geological interpretation and the accuracy of spatial characterization, providing an effective technical basis for gas drainage optimization, geological hazard assessment, and intelligent underground coal mining. Full article

Historic masonry towers are iconic components of the world’s architectural heritage, yet their seismic vulnerability remains to be investigated, particularly regarding the influence of vertical ground motion. This study investigates the seismic response of the Roncole bell tower, a 35 m high slender masonry structure located in Emilia-Romagna, Italy, that experienced severe damage during the 2012 Emilia earthquake sequence, presumably related to the second shock of 29 May, the epicenter of which was within approximately 5 km of the tower. In the absence of direct site recordings, a simplified seismic scenario was reconstructed using accelerograms from two nearby stations and interpolation procedures based on logarithmic attenuation relationships. Nonlinear finite element analyses were performed in Abaqus using a detailed three-dimensional model comprising approximately 263,000 tetrahedral elements and a Concrete Damage Plasticity constitutive law for masonry. Four elastic moduli of the material and multiple seismic input scenarios were considered, with and without inclusion of the vertical seismic component. Modal analysis showed that the tower response is governed by the first two dominant horizontal bending modes and one significant vertical mode involving a high percentage of participating mass. Results indicate that while horizontal excitation controls global sway behavior, the vertical component strongly amplifies axial force fluctuations and vertical displacements located close the tower base and rules the bending capacity of the tower. Nonlinear time-history analyses also revealed residual drifts close to collapse thresholds drifts under most of the scenarios considered. Simulated crack patterns closely matched the actual earthquake damage, at the base of the tower, window openings, and the façade in the tilting side. The study demonstrates that three-component seismic analyses are essential for reliable assessment of historic slender masonry towers subjected to near-source earthquakes. Full article

To address the limitations of the traditional Teaching–Learning-Based Optimization (TLBO) algorithm when solving high-dimensional, multimodal, strongly nonlinear, and constrained global optimization problems—such as single search direction, inefficient population information exchange, insufficient local exploitation capability, susceptibility to premature convergence, and low solution accuracy—this paper proposes a multi-strategy collaborative enhanced Teaching–Learning-Based Optimization algorithm (CSTLBO). While retaining the fundamental two-phase framework of the original TLBO, namely the teacher phase and learner phase, three novel strategies are sequentially incorporated: a Collaborative Differential Guidance (CDG) strategy to enrich global search directions, an Elite-Guided Collaborative Interaction (EGCI) strategy to enhance efficient transmission of high-quality population information, and a Quadratic Interpolation Local Refinement (QILR) strategy to improve fine-grained exploitation in promising regions. Together, these strategies enable an adaptive trade-off between broad search capability and refined local optimization. The effectiveness of CSTLBO is systematically assessed using the CEC2017 and CEC2022 benchmark suites, with comparative analyses conducted against multiple advanced algorithms and the baseline TLBO method. Experimental results demonstrate that CSTLBO exhibits significant superiority in terms of convergence speed, solution accuracy, robustness, and statistical performance, particularly in the 100-dimensional CEC2017 benchmark problems and the WSN deployment problem, while maintaining competitive performance on the 10- and 20-dimensional CEC2022 benchmarks. The superiority of CSTLBO is further validated through the Wilcoxon rank-sum test and Friedman mean rank test. Furthermore, the proposed algorithm is applied to the coverage deployment optimization problem in Wireless Sensor Networks (WSNs), a typical high-dimensional engineering problem involving multiple conflicting deployment indicators, which is formulated as a weighted single-objective optimization problem in this study. The results show that CSTLBO achieves a coverage rate of up to 95.71% with a fitness value as low as 0.1344, outperforming the compared algorithms in overall performance. Owing to its simple structure, low computational complexity, and strong generalization capability, CSTLBO provides an efficient and reliable solution for complex global optimization problems and practical engineering applications. Full article

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy. Full article

Accurate and efficient computation of partial inductances is essential in modern electromagnetic modeling. A three-dimensional cubic spline interpolation method is proposed for efficient evaluation of partial inductances between uniform cubic cells on a Cartesian grid, a common configuration in FFT-accelerated methods, including PEEC. The interpolation dimensionality is chosen to match the three-dimensional nature of the problem. Two reference grid types are investigated: a uniform Cartesian grid and a nonuniform grid with predominantly logarithmic spacing. The results show that the uniform grid does not provide sufficient accuracy in the near region under practical memory constraints, whereas the nonuniform logarithmic grid, augmented with a point at zero, enables accurate evaluation over the entire problem space. The interpolation error is controlled by the number of reference points per decade and the interpolation order, providing a trade-off between accuracy and computational cost. The influence of boundary effects is also analyzed, confirming that they can affect interpolation accuracy and should be considered in practical applications. Simulation results demonstrate that the proposed method achieves maximum relative errors down to approximately 10⁻⁷ with practical memory configurations. It provides accuracy comparable to Tucker reconstruction while offering about 30× faster usage-phase evaluation. In addition, it improves accuracy over one-dimensional spline interpolation while maintaining comparable evaluation speed. Full article

Accurate Structural Health Monitoring of offshore wind turbines is critical for ensuring their long-term operational safety in harsh marine environments. Although displacement is a fundamental metric for assessing structural deformation and stress distribution, its direct measurement in open-ocean conditions is severely hindered by environmental interference and the absence of stable spatial references. Consequently, reconstructing displacement from structural acceleration through double integration is widely adopted, yet it suffers from severe baseline drift. Furthermore, existing drift-mitigation techniques often rely on empirical parameter selection and are limited to single-point reconstructions, failing to capture the full three-dimensional (3D) spatial attitude of the structure. To address these limitations, this paper proposes a novel 3D spatial attitude reconstruction framework based on advanced drift removal and spatial interpolation. First, an improved drift removal algorithm is developed to accurately eliminate baseline errors from acceleration signals, ensuring the physical fidelity of the reconstructed local displacements. Subsequently, cubic spline interpolation is utilized to extrapolate these discrete local measurements into a comprehensive full-field attitude profile of the entire turbine structure. The performance and robustness of the proposed method are systematically verified through numerical simulations and finite element analysis. Finally, its engineering applicability and accuracy are further validated via laboratory experiments and field measurements. The proposed framework effectively mitigates noise sensitivity and significantly enhances the accuracy of full-field attitude reconstruction, providing a reliable foundation for refined structural health assessments of OWTs. Full article

Climate change and rising global temperature values lead to a cascade of effects on human health and well-being. Methodologies for assessing thermal conditions and identifying areas with increased thermal stress are important for enhancing the quality of life in urban environments. This study is aimed at developing a methodology that combines high-resolution simulation data with surface meteorological observations for application in urban thermal stress assessment. Eleven urban public sites within the metropolitan area of Athens, Greece (i.e., squares and parks) were simulated using the three-dimensional microclimate model ENVI-met. The model was validated using micrometeorological data from field campaigns conducted in summer, autumn and winter. The validation results confirmed that ENVI-met showed satisfactory performance for further research analysis. Subsequently, Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) were calculated using data from weather stations operated by the National Observatory of Athens and the Hellenic National Meteorological Service. PET and UTCI were then spatially interpolated using a mixed modeling and kriging method, with parameters optimized based on statistical validation metrics derived from the ENVI-met simulations. Finally, seasonal bioclimatic maps were produced to identify areas experiencing unfavorable thermal conditions. The spatial analysis revealed distinct seasonal patterns in the distribution of unfavorable thermal conditions across the Athens metropolitan area. Full article

Three-dimensional (3D) orebody boundary modeling primarily relies on spatial interpolation methods, such as radial basis functions (RBFs). However, these methods struggle with large datasets and require gradient or normal constraints for stable geometric extrapolation. This study proposes an adaptive finite difference implicit-modeling method, which avoids gradient information and can handle complex 3D orebody boundaries from large-scale, irregular datasets. We utilized difference operators for hanging and constrained octree nodes and applied adaptive density-based smoothing to reduce artifacts from sparse data, enabling complex boundary construction on nearly one million non-uniform control points. We used octree-based convolutional neural networks to fuse spatial features across octree levels, merging points with similar local geometries into the same finest-level cells. This enabled optimal adaptive octree mesh partitioning that accounts for spatial similarity among control points while controlling the total mesh count. Using this adaptive octree mesh, a finite difference scheme suitable for non-uniform mesh structures was constructed. The method outperforms traditional RBFs and uniform-grid finite difference methods in model accuracy, computational efficiency, and memory usage, exhibiting a robust performance across various data distribution patterns. Full article

This study investigates the robustness of Frenet–Serret curvature ($\kappa$) and torsion ($\tau$) estimates derived from noisy discretely-sampled three-dimensional space curves, with emphasis on the comparative performance of cubic spline and cubic Hermite interpolation methods. Accurate estimation of these geometric invariants is essential for reliable analysis of curves arising in signal processing and shape reconstruction; yet, the higher-order derivatives required for their computation exhibit pronounced sensitivity to measurement noise. We examine curves constructed through a Hilbert transform-based parameterization of the form $\mathrm{r}\left(t\right)=\left(X\left(t\right),A\left(t\right)sin\varphi \left(t\right),g\left(t\right)\right),$ where discrete samples are contaminated with additive white Gaussian noise at varying signal-to-noise ratios. Reconstruction is performed using cubic spline interpolation, which ensures global $C^2$ continuity, as well as cubic Hermite spline interpolation, which provides $C^1$ continuity with local tangent control. Frenet frame computations are then applied via regularized finite difference schemes. To characterize noise amplification theoretically, we derive the Curvature Stability Index (CSI) and Torsion Stability Index (TSI) as first-order variance bounds under the delta method. While these indices formalize the derivative-order dependence of noise sensitivity, Monte Carlo simulations reveal that empirical variance exceeds theoretical predictions by factors of ${10}^4$ to ${10}^6$, indicating dominance of nonlinear error propagation. Nevertheless, the indices establish that torsion instability arises fundamentally from third-order derivative structure rather than ground-truth magnitude. Numerical experiments across three geometric regimes constant-invariant helices, variable-curvature helices, and planar curves with identically zero torsion demonstrate that the ratio of the torsion root mean square error to curvature root mean square error consistently ranges from $6.5$ to $9.8$. This disparity persists even in the degenerate planar case, where $\tau \equiv 0$ analytically, confirming that torsion sensitivity is an intrinsic property of the Frenet–Serret formulation. Across all configurations, cubic spline reconstruction yields lower Monte Carlo mean RMSE and reduced empirical variance compared to Hermite spline, providing superior stability for derivative-based invariant estimation. Full article

Four-dimensional medical images introduce the temporal dimension to three-dimensional spatial data, enabling the dynamic characterization of organ motion and providing important support for disease diagnosis and functional assessment. However, due to constraints such as low-dose acquisition and prolonged scanning, the challenges faced in obtaining 4D medical images with high temporal resolution include insufficient spatial sampling, severe motion artifacts, and image blurring. Therefore, generating high-quality and temporally continuous intermediate frames while ensuring patient safety remains a critical challenge in 4D medical image interpolation. To address this issue, we propose the Relation-Aware Bi-directional Motion Estimation Network (RA-BiMENet) for 4D medical image interpolation, which enables the accurate prediction of intermediate frames at arbitrary time points. Specifically, RA-BiMENet consists of two key components: a spatiotemporal transform MLP (TS-MLP) module and a hierarchical spatiotemporal fusion (HSTF) module. The TS-MLP module performs bi-directional motion estimation in a pyramid-recursive manner, where a relation-aware multi-scale MLP (RAM-MLP) unit is introduced to model local correlations and multi-scale dependencies for accurate nonlinear motion estimation. Based on the estimated transformations, the HSTF module hierarchically integrates cross-temporal features through forward warping and self-attention, thereby enhancing local detail restoration while preserving global temporal consistency. Experimental results demonstrate that RA-BiMENet outperforms state-of-the-art methods on multiple quantitative evaluation metrics and is capable of generating high-fidelity and temporally coherent interpolated frames under complex deformation scenarios, validating its effectiveness and superiority for continuous-time 4D medical image interpolation. Full article

Xinyu pear is one of the important pear cultivars in China. Owing to its rich nutritional composition, high quality, and distinctive flavor, it is highly favored in the market. In this study, near-infrared spectroscopy was employed to determine the soluble solid content (SSC) of Xinyu pears. To investigate the influence of spectral preprocessing on SSC prediction, near-infrared spectra of two batches of Xinyu pear samples were collected using the same portable spectrometer under different acquisition parameters, resulting in differences in spectral bands. A linear interpolation method was introduced to the first batch to generate a new dataset to match the dimensionality of the second batch, and a total of three datasets were used. Five preprocessing methods, including moving average smoothing (MA), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (D1), and second derivative (D2), together with three regression models, namely partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN), were systematically evaluated and compared in terms of predictive accuracy. Overall, PLSR achieved the best prediction performance, followed by CNN and SVR. Certain differences in model performance were observed among the three datasets. In general, MA exhibited the best overall performance across different datasets and models. Although SNV and MSC were slightly inferior to MA, they showed relatively stable predictive accuracy. By contrast, prediction models based on derivative spectra generally performed poorly. To further exploit the complementary information contained in differently preprocessed spectra, a four-branch CNN model was constructed using raw spectra, MA-preprocessed spectra, SNV-preprocessed spectra, and MSC-preprocessed spectra as separate inputs. Based on the fused features extracted by the CNN, PLSR and SVR models were subsequently developed. The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively. For the original datasets of the first and second batches, the feature-fusion model outperformed all single-preprocessing models. For the dataset generated by linear interpolation, the predictive performance of the feature-fusion strategy was comparable across the three models; specifically, its accuracy in SVR exceeded that of all single-preprocessing models, while its accuracies in CNN and PLSR surpassed those of most preprocessing methods. These results demonstrate that integrating feature information from spectra subjected to different preprocessing methods is a feasible strategy for improving prediction accuracy. This study provides an effective reference for SSC prediction in Xinyu pears based on portable spectrometers. Full article

Reservoir evaluation and interpretation are fundamental for production and reserve management, and they require, as a key input, an information matrix describing petrophysical properties within the static model. This study provides the scalar distribution of the petrophysical property associated with flow capacity, $kh$ (where k is permeability and h is sample thickness). This distribution is generated in a three-dimensional space from six locally distributed well-log series. These well logs were derived from measurement, evaluation, calibration, and interpretation processes and correspond to producing reservoirs in southeastern Mexico. To characterize the heterogeneity of the well-log data through the Hurst exponent, the structure-function metric was employed. Subsequently, these exponents were distributed throughout the three-dimensional domain of interest by linear interpolation. Finally, pseudo-well logs for the studied reservoir were generated using the random midpoint displacement algorithm. A local petrophysical information matrix for the $kh$ property containing 52,800 pseudo-records was obtained. The resulting information matrix is suitable for capturing both fine- and coarse-scale heterogeneity. The methodology applied here suggests a substantial saving in reservoir analysis as a first approximation to the evaluation model. Full article

This paper presents a hybrid numerical approach, termed the Radial Basis Function–Finite Difference Matrix Operator (RBF–FDMO) method, to enhance the accuracy and flexibility of the conventional FDMO technique for three-dimensional (3D) electromagnetic field analysis governed by the Laplace–Poisson equation. Conventional numerical methods often face challenges related to computational complexity and limited flexibility when handling non-uniform discretization and complex geometries. In the proposed method, spatial derivatives are approximated using RBF-based interpolation rather than finite difference schemes derived from Taylor series expansion. This formulation enables the construction of high-accuracy derivative operators on both uniform and non-uniform FD grids, thereby improving numerical robustness and adaptability to complex geometries. The performance of the proposed method is first compared with the FDMO in a 3D benchmark problem, with reductions of more than two orders of magnitude in both RMS and maximum errors. Furthermore, the RBF-FDMO approach is developed and, for the first time, applied to the analysis of grounding system (GS) configurations specified in IEEE Std. 80™, as well as a practical 110 kV substation GS in Vietnam. The obtained potential distributions, grounding resistances, and touch and step voltages confirm the effectiveness and reliability of the method. The results indicate that the proposed approach features a simple formulation and competitive computational efficiency, positioning it as a practical alternative to conventional methods like the finite element method (FEM) and the boundary element method (BEM) for GS analysis and design. Full article

Within the framework of consistent couple stress theory (CCST) and employing Hamilton’s principle, we derive a Galerkin weak formulation to analyze the three-dimensional (3D) size-dependent free vibration characteristics of a simply supported, functionally graded (FG) magneto-electro-elastic (MEE) doubly curved (DC) microscale shell subjected to a uniform temperature change. Incorporating the differential reproducing kernel (DRK) interpolants into the weak formulation, we further develop an element-free Galerkin (EFG) method. The microscale shell of interest is composed of two-phase MEE materials, and its material properties are assumed to vary through its thickness according to a power-law distribution of the volume fractions of the constituents. The results show that the natural frequency solutions obtained using the EFG method are in excellent agreement with the reported 3D solutions for laminated composite and FG-MEE macroscale plates, with the material length-scale parameter and the inverse of the curvature radii set to zero. The effects of the material length-scale parameter, temperature change, inhomogeneity index, and mid-surface radius and length-to-thickness ratios on the FG-MEE microscale shell’s free vibration characteristics in a thermal environment are examined and appear to be significant. Full article