Search Results (230)

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

LiDAR-based Simultaneous Localization and Mapping (SLAM) serves as a fundamental technology for autonomous navigation. However, in complex environments, LiDAR odometry often experience degraded localization accuracy and robustness. This paper proposes a computationally efficient enhancement strategy for LiDAR odometry, which improves system performance by reinforcing high-quality features throughout the optimization process. For non-ground features, the method employs statistical geometric analysis to identify stable points and incorporates a contribution-weighted optimization scheme to strengthen their impact in point-to-plane and point-to-line constraints. In parallel, for ground features, locally stable planar surfaces are fitted to replace discrete point correspondences, enabling more consistent point-to-plane constraint formulation during ground registration. Experimental results on the KITTI and M2DGR datasets demonstrated that the proposed method significantly improves localization accuracy and system robustness, while preserving real-time performance with minimal computational overhead. The performance gains were particularly notable in scenarios dominated by unstructured environments. Full article

In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image. Full article

This paper introduces and analyzes a novel fractional-order Ananthakrishna model. The stability of its equilibrium points is first investigated using fractional-order stability criteria, particularly in regions where the corresponding integer-order model exhibits instability. A linear finite difference scheme is then developed, incorporating an accelerated L1 method for the fractional derivative. This enables a detailed exploration of the model’s dynamic behavior in both the time domain and phase plane. Numerical simulations, including Lyapunov exponents, bifurcation diagrams, phase and time diagrams, demonstrate that the fractional model exhibits stable and periodic behaviors across various fractional orders. Notably, as the fractional order approaches a critical threshold, the time required to reach stability increases significantly, highlighting complex stability-transition dynamics. The computational efficiency of the proposed scheme is also validated, showing linear CPU time scaling with respect to the number of time steps, compared to the nearly quadratic growth of the classical L1 and Grünwald-Letnikow schemes, making it more suitable for long-term simulations of complex fractional-order models. Finally, four types of stress-time curves are simulated based on the fractional Ananthakrishna model, corresponding to both stable and unstable domains, effectively capturing and interpreting experimentally observed repeated yielding phenomena. Full article

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches. Full article

The interactive grasp of robots can grasp the corresponding objects according to the user’s choice. Most interactive grasp methods based on deep learning comprise visual language and grasp detection models. However, in existing methods, the trainability and generalization ability of the visual language model is weak, and the robot cannot cope well with grasping small target objects. Therefore, this paper proposes a 6-DoF grasp detection method guided by visual language, which converts text instructions and RGBD images of the scene to be grasped into inputs and outputs for the 6-DoF grasp posture of the object corresponding to the text instructions. In order to improve the trainability and feature extraction ability of the visual language model, a multi-head attention mechanism combined with hybrid normalization is designed. At the same time, a local attention mechanism is introduced into the grasp detection model to enhance the global and local information interaction ability of point cloud data, thereby improving the grasping ability of the grasp detection model for small target objects. The method proposed in this paper first uses the improved visual language model to predict the plane position information of the target object, then uses the improved grasp detection model to predict all the graspable postures in the scene, and finally uses the plane position information to filter out the graspable postures of the target object. The visual language model and grasp detection model proposed in this paper have achieved excellent performance in various scenarios of public datasets while ensuring a specific generalization ability. In addition, we also conducted real grasp experiments, and the 6-DoF grasp detection method based on visual language guidance proposed in this paper achieved a grasp success rate of 95%. Full article

To address the problem of cooperative multi-vehicle operations targeting critical objectives in three-dimensional space, this paper proposes a variable-power adaptive fast sliding-mode guidance law. First, a dynamic vehicle–target model is established in three-dimensional space by using the projection of the horizontal vehicle–target plane to determine the reference plane. The guidance law design is projected onto the reference plane based on the average consensus convergence control method. This not only drives the estimated deviations to zero to achieve time consistency but also ensures that the estimated time-to-go converges to the actual time-to-go. Additionally, an adaptive input compensation component is designed in the vertical line-of-sight (LOS) direction, driving the aspect angle to converge to zero before the final point is reached. Furthermore, in the vertical reference plane direction, an adaptive variable-power sliding-mode control method is designed based on the traditional sliding-mode control scheme, in which the corresponding power exponent is selected according to the variation exhibited by the sliding-mode values. On the one hand, higher power levels can result in faster convergence; on the other hand, lower power levels can be associated with better stability. Compared with the traditional sliding-mode control technique, the proposed method achieves guaranteed stable convergence at the end of the control process, with a 49% improvement in convergence efficiency. Finally, simulations verify the designed three-dimensional guidance law, demonstrating that it features fast convergence, high stability, and high precision. Full article

Based on Life Cycle Assessment (LCA) and ISO standards, we compared the global environmental sustainability (ES) of two technologies that process the organic fraction of municipal solid waste (OFMSW) in Mexico. The first technology was a biorefinery (BRF) known as HMEZSNN-BRF (abbreviation for Hydrogen-Methane-Extraction-Enzyme-Saccharification/Nanoproduction Biorefinery); it produces the gas biofuels hydrogen (H) and methane (M), organic acids (E), enzymes (Z), saccharified liquors (S), and bionanobioparticles (BNBPs) in a nanoproduction stage (NN). The second technology was incineration with energy recovery (IER). An LCA was performed with a functional unit (FU) of 1000 kg of OFMSW. The BRF generates 166.4 kWh/FU (600 MJ) of net electricity, along with bioproducts such as volatile organic acids (38 kg), industrial enzyme solution (1087 kg), and BNBPs (40 kg). The IER only produces 393 net kWh/FU electricity and 5653 MJ/FU heat. The characterization potential environmental impacts (PEIs) were assessed using SimaPro software, and normalized PEIs (NPEIs) were calculated accordingly. We defined a new variable alpha and the indices σ-τ plane for quantifying the ES. The higher the alpha, the lower the ES. Alpha was the sum of the eighteen NPEIs aligned with the ISO standards. The contributions to PEI and NPEI were also analyzed. Four NPEIs were the highest in both technologies, i.e., freshwater and marine ecotoxicities and human non-carcinogenic and carcinogenic toxicities. For the three first categories, the NPEI values corresponding to IER were much higher than those of the BRF (58.6 and 8.7 person*year/FU freshwater toxicity; 93.5 and 13.6 marine ecotoxicity; 12.1 and 1.8 human non-carcinogenic toxicity; 13.7 and 13.9 human carcinogenic toxicity, for IER and the BRF, respectively). The total α values were 179.1 and 40.7 (person*yr)/FU for IER and the BRF, respectively. Thus, the ES of IER was four times lower than that of the BRF. Values of σ = 0.592 and τ = −0.368 were found; the point defined by these coordinates in the σ-τ plane was located in Quadrant IV. This result confirmed that the BRF in this work is more environmentally sustainable (with restrictions) than the IER in Mexico for the treatment of the OFMSW. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article

The application prospects of multi-temporal 3D models are broad. It is difficult to ensure that multi-temporal 3D models have a consistent spatial reference. In this study, a method for automatic alignment of multi-temporal 3D models based on phase congruency (PC) matching is proposed. Firstly, the texture image of the multi-temporal 3D model is obtained, and the key points are extracted from the texture image. Secondly, the affine model between the plane of the key point and its corresponding tile triangle is established, and the 2D coordinates of the key point are mapped to 3D spatial coordinates. Thirdly, multi-temporal 3D model matching is completed based on PC to obtain a large number of evenly distributed corresponding points. Finally, the parameters of the 3D transformation model are estimated based on the multi-temporal corresponding points, and the vertex update of the 3D model is completed. The first experiment demonstrates that the method proposed in this study performs remarkably well in improving the positioning accuracy of feature point coordinates, effectively reducing the mean error of the systematic error to below 0.001 m. The second experiment further reveals the significant impact of different 3D transformation models. The experimental results show that the coordinates obtained based on position and orientation system (POS) data have significant positioning errors, while the method proposed in this study can reduce the coordinate errors between the two-period models. Due to the fact that this method does not require obtaining ground control points (GCPs) and does not require manual measurement for 3D geometric registration, its application to multi-temporal 3D models can ensure high-precision spatial referencing for multi-temporal 3D models, streamlining processes to reduce resource intensity and enhancing economic efficiency. Full article

This work aims to study the compressive buckling and consequent blowup of jointed concrete pavements due to thermal rise. For this purpose, a simple and effective mixed FEM, originally introduced for performing static and buckling analyses of beams on elastic supports, is extended for performing a preliminary study of jointed concrete pavements. An elastic Euler–Bernoulli beam in frictionless and bilateral contact with an elastic support is considered. Three different elastic support models are assumed, namely a Winkler support, an elastic half-space (3D), and half-plane (2D). The transversal pavement joint or crack is modeled employing a hinge at the beam midpoint with nil rotational stiffness. Numerical tests are performed by determining critical loads and the corresponding modal shapes, with particular attention to the first minimum critical load related to pavement blowup. From a theoretical point of view, the results show that minimum critical loads converge to existing results in the case of Winkler support, whereas new results are obtained in the case of the 2D and 3D support types. Associated modal shapes have maximum upward displacements at the beam midpoint. The second and subsequent critical loads, together with the corresponding sinusoidal modal shapes, converge to existing results. From a practical point of view, minimum critical loads represent a lower bound for estimating axial forces due to thermal variation causing jointed pavement blowup. Full article

In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization. Full article

This article considers a class of entire functions of several complex variables that are bounded in the Cartesian product of some half-planes. Each such hyperplane is defined on the condition that the real part of the corresponding variable is less than some r. For this class of functions, there are established analogs of the Wiman theorems. The first result describes the behavior of an entire function from the given class at the neighborhood of the point of the supremum of its modulus. The second result shows asymptotic equality for supremums of the modulus of the function and its real part outside some exceptional set. In addition, the analogs of Wiman’s theorem are obtained for entire multiple Dirichlet series with arbitrary non-negative exponents. These results are obtained as consequences of a new statement describing the behavior of an entire function $F\left(z\right)$ of several complex variables $z=(z_1,\dots ,z_p)$ at the neighborhood of a point w, where the value $F\left(w\right)$ is close to the supremum of its modulus on the boundary of polylinear domains. The paper has two moments of novelty: the results use a more general geometric exhaustion of p-dimensional complex space by polylinear domains than previously known; another aspect of novelty concerns the results obtained for entire multiple Dirichlet series. There is no restriction that every component of exponents is strictly increasing. These statements are valid for any non-negative exponents. Full article

To address the challenge of measuring the plate wall offset at the U-shaped groove positions after assembling large cylindrical shell arc segments, this paper proposes a measurement method based on multi-line-structured light vision sensors. The sensor is designed and calibrated to collect U-shaped groove workpiece images containing multiple laser stripes. The central points of the laser stripes are extracted and matched to their corresponding light plane equations to obtain local point cloud data of the measured positions. Subsequently, point cloud data from the plate wall regions on both sides of the groove are separated, and the plate wall offset is calculated using the local distance computation method between planes in space. The experimental results demonstrate that, when measuring a standard sphere with a diameter of 30 mm from multiple angles, the measurement uncertainty is ±0.015 mm within a 95% confidence interval. Within a measurement range of 155 mm × 220 mm × 80 mm, using articulated arm measurements as reference values, the plate wall offset measurement uncertainty of the multi-line-structured light vision sensor is ±0.013 mm within a 95% confidence interval, showing close agreement with reference values. Full article

It is shown that it is reasonable to use Galois fields, including those obtained by algebraic extensions, to describe the position of a point in a discrete Cartesian coordinate system in many cases. This approach is applicable to any problem in which the number of elements (e.g., pixels) into which the considered fragment of the plane is dissected is finite. In particular, it is obviously applicable to the processing of the vast majority of digital images actually encountered in practice. The representation of coordinates using Galois fields of the form GF(p²) is a discrete analog of the representation of coordinates in the plane through a complex variable. It is shown that two different types of algebraic extensions can be used simultaneously to represent transformations of discrete Cartesian coordinates described through Galois fields. One corresponds to the classical scheme, which uses irreducible algebraic equations. The second type proposed in this report involves the use of a formal additional solution of some equation, which has a usual solution. The correctness of this approach is justified through the representation of the elements obtained by the algebraic expansion of the second type by matrices defined over the basic Galois field. It is shown that the proposed approach is the basis for the development of new methods of information protection, designed to control groups of UAVs in the zone of direct radio visibility. The algebraic basis of such methods is the solution of systems of equations written in terms of finite algebraic structures. Full article