Search Results (68)

Accurate estimation of tree diameter at breast height (DBH) from LiDAR point clouds is essential for forest inventory, biomass assessment, and ecological monitoring. This paper presents a perimeter-based DBH estimation framework that achieves competitive accuracy against geometric fitting methods across three datasets. The proposed approach partitions the trunk cross-section into angular sectors and employs Gaussian Mixture Models (GMMs) to identify representative boundary points in each sector, weighted by radial proximity and statistical confidence. To handle occlusion and partial scans, missing sectors are reconstructed using symmetry-aware proxy generation. The final perimeter is modeled via either convex hull or B-spline interpolation, from which DBH is derived. Extensive experiments were conducted on two public TreeScope datasets and a custom mobile LiDAR dataset. Compared to the Density-Based Clustering Ring Extraction (DBCRE) baseline, our method reduced RMSE by 22.7% on UCM-0523M (from 2.60 to 2.01 cm), 34.3% on VAT-0723M (from 3.50 to 2.30 cm), and 29.6% on the Custom Dataset (from 2.16 to 1.52 cm). Ablation studies confirmed the individual and synergistic contributions of GMM clustering, radial consistency filtering, and proxy synthesis. Overall, the method provides a flexible alternative that reduces dependence on strict geometric assumptions, offering improved DBH estimation performance with moderate occlusion and incomplete, uneven boundary coverage. Full article

To enhance the intelligence and digital management level of radio telescopes and ensure the safe and stable operation of antennas, this paper proposes a real-time state evaluation method for the antenna structure of radio telescopes based on digital twin (DT) technology. Firstly, based on the five-dimensional model of DT, a digital twin system (DTs) framework for radio telescopes is designed. Secondly, the quadric error metrics (QEM) mesh-simplification algorithm and mesh-reconstruction technology are employed to obtain a lightweight twin model of the antenna. Furthermore, a random forest (RF) regression surrogate model is established using finite element point cloud data samples. The K-nearest neighbor (KNN) algorithm and radial basis function (RBF) interpolation algorithm are utilized to construct the virtual–physical mapping model of the antenna, enabling rapid prediction and evaluation of the antenna structure state. Finally, a DT for real-time antenna structure state evaluation is developed using the Unity3D engine, with an experimental prototype of a reflector antenna as the object. Experimental results show that the average prediction accuracy of the physical field surrogate model of the system is 0.98, and the average computation time is 0.4 s. The system meets the precision and computational efficiency requirements for the real-time and accurate evaluation of the antenna structure state. Full article

Advances in material science and intelligent systems have led to an increasing use of large-deflection flexible structures in the aerospace industry, including flexible-wall wind tunnel nozzles, deformable wings, and variable nozzles for aircraft engines. These structures have attracted significant research interest due to their variable aerodynamic performance, functional diversity, and dynamic response characteristics that distinguish them from rigid structures. Large-deflection flexible aerodynamic structures typically consist of flexible structural surfaces and actuators. Precise deformation control and optimized structural design are crucial for achieving their full performance potential. However, few existing technological tools can effectively guide the implementation of such deformation control and optimized design. In this paper, we first established a mechanical model of a multi-pivot flexible nozzle based on a typical wind tunnel flexible nozzle. We then derived a theoretical model of beam deformation with multi-point dynamic constraints using the principle of variability. Next, we created a deformation solution method based on radial basis point interpolation to evaluate nozzle profile accuracy. Finally, we established a complete surrogate-based optimization process for a large-deflection flexible nozzle and experimentally verified it using a wind tunnel nozzle prototype equipped with laser tracking and flexible sensors. The results show that the nozzle’s profile accuracy remains within ±0.2 mm under specified operational conditions. Full article

High-precision three-dimensional geological modeling of mining faces is crucial for intelligent coal mining and disaster prevention. Accurate spatial interpolation is essential for building high-quality models. This study focuses on the 25214 workface of the Hongliulin coal mine, addressing challenges in interpolating terrain elevation, stratum thickness, and coal seam thickness data. We evaluate eight interpolation methods (four kriging methods, an inverse distance weighting method, and three radial basis function methods) for terrain and stratum thickness, and nine methods (including the Bayesian Maximum Entropy method) for coal seam thickness, using cross-validation to assess their accuracy. Research results indicate that for terrain elevation data with dense and evenly distributed sampling points, linear kriging achieves the highest accuracy (MAE = 1.01 m, RMSE = 1.20 m). For the optimal interpolation methods of five layers of thickness data with sparse sampling points, the results are as follows: Q₄, spherical kriging (MAE = 2.13 m, RMSE = 2.83 m); N₂b, IDW (p = 2), MAE = 2.08 m, RMSE = 2.44 m; J₂y³, RS-RBF (MAE = 0.89 m, RMSE = 1.05 m); J₂y², TPS-RBF (MAE = 1.96 m, RMSE = 2.25 m); J₂y¹, HS-RBF (MAE = 2.36 m, RMSE = 2.71 m). A method for accurately delineating the zero line of strata thickness by assigning negative values to virtual thickness in areas of missing strata has been proposed. For coal seam thickness data with uncertain data (from channel wave exploration), a soft-hard data fusion interpolation method based on Bayesian Maximum Entropy has been introduced, and its interpolation results (MAE = 0.64 m, RMSE = 0.66 m) significantly outperform those of eight other interpolation algorithms. Using the optimal interpolation methods for terrain, strata, and coal seams, we construct a high-precision three-dimensional geological model of the workface, which provides reliable support for intelligent coal mining. Full article

This paper proposes a specifically parametric design method for planing hulls using longitudinal functions and shape coefficients in order to meet the requirements for optimizing the hydrodynamic performance of planing hulls. To fully define the geometry of the planing hull, a series of design parameters and a set of longitudinal functions and shape coefficients are introduced to define key geometric features. The main frame curves of the hull are designed from bottom to top to ensure the priority and independence of parameters related to the planing surface. The mathematical equations of the control points of the keel curve, chine curve, sheer curve, and surface station curve of the hull framework are established and solved based on B-spline theory. This configures the basis for generating a continuous smooth surface of the hull. Finally, based on the frame curves, the hull surface was generated by using NURBS surface interpolation. The design parameters, especially the longitudinal functions and shape coefficients, can intuitively and independently control the key features of the hull form, which allow control over key geometric features that are highly relevant to the hydrodynamics of the planing hull. By utilizing this approach, rapid production of deep-V and radial planing hulls is achievable, resulting in closed and smooth hull surfaces. Case studies have provided evidence that the modeling of monohull unstepped planing hulls with diverse characteristics can be effectively accomplished through the definition of these parameters. Full article

In sensor design, electromagnetic field numerical simulation techniques are widely used to investigate the working principles of sensors. These analyses help designers understand how sensors detect and respond to external signals during operation. One popular method for electromagnetic field computation is the meshless radial point interpolation method (RPIM), where the number and distribution of nodes are critical to ensuring both accuracy and efficiency. However, traditional RPIM methods often face challenges in achieving stable and precise results, particularly in complex electromagnetic environments. In order to enhance the stability and accuracy of electromagnetic numerical calculations, a node generation and adaptive refinement algorithm for the meshless RPIM is proposed. The proposed approach includes an initial node-generation method designed to optimize the balance between computational accuracy and efficiency, as well as a dynamic error threshold and hybrid node refinement method to precisely identify and adaptively refine areas requiring additional nodes, ensuring high precision in critical regions. The proposed method was validated through its application to electrostatic fields and multi-media magnetic fields, demonstrating significant improvements in both stability and accuracy compared with conventional RPIM approaches. These findings highlight the potential of the proposed algorithm to enhance the reliability and precision of electromagnetic field simulations in sensor design and related applications. Full article

This study introduces methods for generating fused precipitation data by applying radial basis function (RBF) interpolation, which integrates radar reflectivity-based data with ground-based precipitation gauge measurements. Rain gauges provide direct point rainfall measurements near the ground, while radars capture the spatial variability of precipitation. However, radar-based estimates, particularly for extreme rainfall events, often lack accuracy due to their indirect derivation from radar reflectivity. The study aims to produce high-resolution gridded ground precipitation data by merging radar rainfall estimates with the precise rain gauge measurements. Rain gauge data were sourced from automated synoptic observing systems (ASOSs) and automatic weather systems (AWSs), while radar data, based on hybrid surface rainfall (HSR) composites, were all provided by the Korea Meteorological Administration (KMA). Although RBF interpolation is a well-established technique, its application to the merging of radar and rain gauge data is unprecedented. To validate the accuracy of the proposed method, it was compared with traditional approaches, including the mean field bias (MFB) adjustment method, and kriging-based methods such as regression kriging (RK) and kriging with external drift (KED). Leave-one-out cross-validation (LOOCV) was performed to assess errors by analyzing overall error statistics, spatial errors, and errors in rainfall intensity data. The results showed that the RBF-based method outperformed the others in terms of accuracy. Full article

In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by the stratigraphic structural model. A key innovation is the incorporation of a well-sampled geological stratigraphical potential field (SPF) as an ancillary variable, which enhances the interpolation of geological properties in areas with sparse and uneven sampling points. The proposed MRBF method outperforms traditional interpolation techniques by showing reduced dependency on the distribution of sampling points. Furthermore, the study calculates the bearing capacity of individual pile foundations based on precise stratigraphic thicknesses, yielding more accurate results compared to conventional methods that average these values across the entire site. Additionally, the integration of 3D geological models with urban planning facilitates the development of comprehensive urban digital twins, optimizing resource management, improving decision-making processes, and contributing to the realization of smart cities through more efficient data-driven urban management strategies. Full article

Today, topological structural optimisation is a valuable computational technique for designing mechanical components with optimal mass-to-stiffness ratios. Thus, this work aims to assess the performance of the Radial Point Interpolation Method (RPIM) when compared with the well-established Finite Element Method (FEM) within the context of a vehicle suspension control arm’s structural optimisation process. Additionally, another objective of this work is to propose an optimised design for the suspension control arm. Being a meshless method, RPIM allows one to discretise the problem’s domain with an unstructured nodal distribution. Since RPIM relies on a weak form equation to establish the system of equations, it is necessary to additionally discretise the problem domain with a set of background integration points. Then, using the influence domain concept, nodal connectivity is established for each integration point. RPIM shape functions are constructed using polynomial and radial basis functions with interpolating properties. The RPIM linear elastic formulation is then coupled with a bi-evolutionary bone remodelling algorithm, allowing for non-linear structural optimisation analyses and achieving solutions with optimal stiffness/mass ratios. In this work, a vehicle suspension control arm is analysed. The obtained solutions were evaluated, revealing that RPIM allows better solutions with enhanced truss connections and a higher number of intermediate densities. Assuming the obtained optimised solutions, four models are investigated, incorporating established design principles for material removal commonly used in vehicle suspension control arms. The proposed models showed a significant mass reduction, between 18.3% and 31.5%, without losing their stiffness in the same amount. It was found that the models presented a stiffness reduction between 5.4% and 9.8%. The obtained results show that RPIM is capable of delivering solutions similar to FEM, confirming it as an alternative numerical technique. Full article

Topological structural optimization is a powerful computational tool that enhances the structural efficiency of mechanical components. It achieves this by reducing mass without significantly altering stiffness. This study combines the Natural-Neighbour Radial-Point Interpolation Method (NNRPIM) with a bio-inspired bi-evolutionary bone-remodelling algorithm. This combination enables non-linear topological optimization analyses and achieves solutions with optimal stiffness-to-mass ratios. The NNRPIM discretizes the problem using an unstructured nodal distribution. Background integration points are constructed using the Delaunay triangulation concept. Nodal connectivity is then imposed through the natural neighbour concept. To construct shape functions, radial point interpolators are employed, allowing the shape functions to possess the delta Kronecker property. To evaluate the numerical performance of NNRPIM, its solutions are compared with those obtained using the standard Finite Element Method (FEM). The structural optimization process was applied to a practical example: a vehicle’s suspension control arm. This research is divided into two phases. In the first phase, the optimization algorithm is applied to a standard suspension control arm, and the results are closely evaluated. The findings show that NNRPIM produces topologies with suitable truss connections and a higher number of intermediate densities. Both aspects can enhance the mechanical performance of a hypothetical additively manufactured part. In the second phase, four models based on a solution from the optimized topology algorithm are analyzed. These models incorporate established design principles for material removal commonly used in vehicle suspension control arms. Additionally, the same models, along with a solid reference model, undergo linear static analysis under identical loading conditions used in the optimization process. The structural performance of the generated models is analyzed, and the main differences between the solutions obtained with both numerical techniques are identified. Full article

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM_2.5 (PM_2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM_2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM_2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM_2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m³ to 0.004 μg/m³. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m² did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors. Full article

This paper presents a new means to control the processes involving energy conversion. Electric machines fed by electronic converters provide a useful power defined by the inner product of two generalized energetic variables: effort and flow. The novelty in this paper is controlling the desired energetic variables by a Data-Driven Control (DDC) law, which comprises the effort and flow and the corresponding process control. The same desired useful power might be obtained with different controls at different efficiencies. Solving the regularization problem is based on building a knowledge database that contains the maximum efficiency points. Knowing a reasonable number of optimal efficiency operation points, an interpolation Radial Base Function (RBF) control was built. The RBF algorithm can be found by training and testing the optimal controls for any admissible operation points of the process. The control scheme developed for Permanent Magnet Synchronous Motor (PMSM) has an inner DDC loop that performs converter control based on measured speed and demanded torque by the outer loop, which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) of the PMSM highlights the advantages of the new control method: the method is free from the process nature and guarantees higher efficiency. Full article

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions. Full article

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R² of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R² of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction. Full article

This work investigates the mechanical behaviour of sandwich beams with cellular cores using a multiscale approach combined with a meshless method, the Natural Neighbour Radial Point Interpolation Method (NNRPIM). The analysis is divided into two steps, aiming to analyse the efficiency of NNRPIM formulation when combined with homogenisation techniques for a multiscale computational framework of large-scale sandwich beam problems. In the first step, the cellular core material undergoes a controlled modification process in which circular holes are introduced into bulk polyurethane foam (PUF) to create materials with varying volume fractions. Subsequently, a homogenisation technique is combined with NNRPIM to determine the homogenised mechanical properties of these PUF materials with different porosities. In this step, NNRPIM solutions are compared with high-order FEM simulations. While the results demonstrate that RPIM can approximate high-order FEM solutions, it is observed that the computational cost increases significantly when aiming for comparable smoothness in the approximations. The second step applies the homogenised mechanical properties obtained in the first step to analyse large-scale sandwich beam problems with both homogeneous and functionally graded cores. The results reveal the capability of NNRPIM to closely replicate the solutions obtained from FEM analyses. Furthermore, an analysis of stress distributions along the beam thickness highlights a tendency for some NNRPIM formulations to yield slightly lower stress values near the domain boundaries. However, convergence towards agreement among different formulations is observed with mesh refinement. The findings of this study show that NNRPIM can be used as an alternative numerical method to FEM for analysing sandwich structures. Full article