Search Results (82)

Spot melting is an emerging alternative to traditional line melting in electron beam powder bed fusion, dividing a layer into thousands of individual spots. This method allows for an almost infinite number of spot arrangements and spot melting sequences to tailor material and part properties. To enhance the productivity of spot melting, the number of spots can be reduced by increasing the beam diameter. However, this results in rough surfaces due to the staircase effect. The classical approach to counteract these effects is to melt a contour that surrounds the infill area. Creating effective contours is challenging because the melted area ought to cover the artifacts from the staircase effect and avoid porosity in the transition area between the infill and contour, all while minimizing additional energy and melt time. In this work, we propose an algorithm for generating a spot melting sequence for contour lines surrounding the infill area. Additionally, we compare three different approaches for combining the spot melting of infill and contour areas, each utilizing a combination of large infill spots and small contour spots. The quality of the contours is evaluated based on optical inspection as well as the porosity between infill and contour using electron optical images, balanced against the additional energy input. The most suitable approach is used to build a complex brake caliper. Full article

The matrix transfer function (MTF) is fundamental to the analysis and control of multivariable descriptor systems, especially under zero initial conditions. Its importance lies in its direct relation to input–output behavior and its natural use in frequency-domain methods. Unlike classical approaches that obtain MTF through companion linearizations or indirect Weierstrass–Kronecker reductions, our method derives a closed-form MFD directly from the descriptor pencil $\left(\lambda \mathit{E}-\mathit{A}\right)$, avoiding linearizations and preserving descriptor structure. This yields (i) an explicit parameterization of state feedback gains via finite/infinite Jordan pairs, (ii) a normalization law that removes impulsive modes by design, and (iii) improved reproducibility through block-polynomial operations suited to large-scale MIMO systems. The framework further extends eigenstructure assignment to descriptor models, combining clarity of analysis with practical control design. These results establish a systematic basis for scalable methods in MIMO descriptor systems. Full article

The harmonic balance method (HBM) is a widely used method for determining the forced response of non-linear systems such as bladed disks. This paper focuses on analyzing the sensitivity of this method to key computational parameters and its robustness. HBM and HBM coupled with pseudo arc length continuation are used in this paper to solve the equation of motion of a test case. The pseudo arc length continuation is necessary because when intermittent contact occurs, natural continuation cannot guarantee solver convergence. Intermittent contact, in addition to turning points, introduces further problems, which are caused by an infinite sequence of decaying, but not zero, Fourier coefficients. This results in the need to oversample the non-linear force time signal to avoid convergence problems. The computational parameters investigated in this paper are the samples per period, which determine the number of points in which the time signal is discretized, and the harmonic truncation order. In addition, the connection of contact parameters, such as friction and contact stiffness, with computational parameters is analyzed. This study shows that the number of time samples per period is the most limiting parameter when intermittent contact occurs; whereas, in the absence of intermittent contact convergence, problems can be avoided with a reasonable number of time points. Poor discretization of the signal leads to a bad computation of Fourier coefficients and thus a lack of convergence. Sensitivity analysis shows that the samples per period depend on the contact parameters, especially normal stiffness. To ensure the solver robustness, it is important to set the computation parameters appropriately to ensure the convergence of the solver while avoiding unnecessary computation effort. Full article

This paper presents an improved coupled radial basis function (ICRBF) approach for solving inverse steady-state heat conduction problems. The proposed method combines infinitely smooth Gaussian radial basis functions with a real-valued mth-order conical spline, where m serves as a coupling index. Unlike the original coupled RBF approach, which relied on multiquadric RBFs paired with a fixed fifth-order spline or later integer-order extensions, our real-order spline generalization enhances accuracy and simplifies the tuning of m. We present a particle swarm optimization approach to optimize the coupling index m. This work represents the first application of the CRBF framework to inverse steady-state heat conduction problems. The ICRBF methodology addresses three key limitations of traditional RBF frameworks: (1) it resolves the persistent issue of shape parameter selection in global RBF methods; (2) it inherently produces well-posed linear systems that can be solved directly, avoiding the need for the regularization typically required in inverse problems; and (3) it delivers superior accuracy compared to existing approaches. Extensive numerical experiments on benchmark problems demonstrate that the proposed method achieves high accuracy and robust numerical stability in solving steady-state heat conduction Cauchy inverse problems, even under significant noise contamination. Full article

The significant capacitive effects of cables can cause resonance stability issues, making it crucial to accurately model cables in the wide frequency range (up to several kilo-Hertz) where resonance typically occurs. To address the complexity and the neglect of cable bonding and earthing arrangements in previous accurate cable modeling, this paper derives a concise analytical method for calculating cable electrical parameters over the wide frequency range, simplifying the prior complex formulas, clarifying the series impedance components, and comprehensively considering three common bonding and earthing arrangements. The case studies of three-core and single-core submarine cables are presented to verify the effectiveness of the improved analytical method. The analysis includes frequency-dependent per-unit-length parameters and the impact of each component on the series impedances. Furthermore, a simplified algorithm is explored, avoiding Bessel function computations based on the impedance component impact study, as well as infinite series calculations by considering the effect of the earth/sea return path position factor on the simplified series accuracy. Full article

This paper examines the foundational concept of random variables in probability theory and statistical inference, demonstrating that their mathematical definition requires no reference to randomization or hypothetical repeated sampling. We show how measure-theoretic probability provides a framework for modeling populations through distributions, leading to three key contributions. First, we establish that random variables, properly understood as measurable functions, can be fully characterized without appealing to infinite hypothetical samples. Second, we demonstrate how this perspective enables statistical inference through logical rather than probabilistic reasoning, extending the reductio ad absurdum argument from deductive to inductive inference. Third, we show how this framework naturally leads to an information-based assessment of statistical procedures, replacing traditional inference metrics that emphasize bias and variance with information-based approaches that describe the families of distributions used in parametric inference better. This reformulation addresses long-standing debates in statistical inference while providing a more coherent theoretical foundation. Our approach offers an alternative to traditional frequentist inference that maintains mathematical rigor while avoiding the philosophical complications inherent in repeated sampling interpretations. Full article

Learning from a nonstationary data stream is challenging, as a data stream is generally considered to be endless, and the learning model is required to be constantly amended for adapting the shifting data distributions. When it meets multi-label data, the challenge would be further intensified. In this study, an adaptive online weighted multi-label ensemble learning algorithm called MLDME (multi-label learning with distribution matching ensemble) is proposed. It simultaneously calculates both the feature matching level and label matching level between any one reserved data block and the new received data block, further providing an adaptive decision weight assignment for ensemble classifiers based on their distribution similarities. Specifically, MLDME abandons the most commonly used but not totally correct underlying hypothesis that in a data stream, each data block always has the most approximate distribution with that emerging after it; thus, MLDME could provide a just-in-time decision for the new received data block. In addition, to avoid an infinite extension of ensemble classifiers, we use a fixed-size buffer to store them and design three different dynamic classifier updating rules. Experimental results for nine synthetic and three real-world multi-label nonstationary data streams indicate that the proposed MLDME algorithm is superior to some popular and state-of-the-art online learning paradigms and algorithms, including two specifically designed ones for classifying a nonstationary multi-label data stream. Full article

Online path planning for UAVs that are following a moving target is a critical component in applications that demand a soft landing over the target. In highly dynamic situations with accelerating targets, the classical potential field (PF) method, which considers only the relative positions and/or velocities, cannot provide precision tracking and landing. Therefore, this work presents an improved acceleration-based potential field (ABPF) path planning method. This approach incorporates the relative accelerations of the UAV and the target in constructing an attractive field. By controlling the acceleration, the ABPF produces smoother trajectories and avoids sudden changes in the UAV’s motion. The proposed approach was implemented in different simulated scenarios with variable acceleration paths (i.e., circular, infinite, and helical). The simulation demonstrated the superiority of the proposed approach over the traditional PF. Moreover, similar path scenarios were experimentally evaluated using a quadrotor UAV in an indoor Vicon positioning system. To provide reliable estimations of the acceleration for the suggested method, a non-linear complementary filter was used to fuse information from the drone’s accelerometer and the Vicon system. The improved PF method was compared to the traditional PF method for each scenario. The results demonstrated a 50% improvement in the position, velocity, and acceleration accuracy across all scenarios. Furthermore, the ABPF responded faster to merging with the target path, with rising times of 1.5, 1.6, and 1.3 s for the circular, infinite, and helical trajectories, respectively. Full article

We present—in the framework of classical theory—a self-consistent derivation scheme which produces equations for the calculation of add-ons to the full field energy and to the effective mass of a charge moving with acceleration, which may be practically used for analyses in various scenarios. The charge is treated as a quasi-point-like charge; this helps to resolve the complications of the “infinite” electromagnetic energy, which are avoided by the procedure of slightly “spreading” the charge. As a result, the concept of the size of the particle takes a straightforward physical interpretation. Indeed, it is within the charge spread, at scales smaller than Compton’s length, where the quantum-field-mechanics approach to be applied. Beyond this region, no “infinite” tails of quantities accumulate. The seeming divergences of the integrals at the upper limits are not physical if one takes into account that the charge moves with acceleration only for a finite duration of time; every real physical process has its beginning and its end. The key focus of this paper is on the methodological aspects of the calculations. Full article

We realize that a Sierpiński arrowhead curve (SAC) fills a Sierpiński gasket (SG) in the same manner as a Peano curve fills a square. Namely, in the limit of an infinite number of iterations, the fractal SAC remains self-avoiding, such that $SAC\subset SG$. Therefore, SAC differs from SG in the same sense as the self-avoiding Peano curve $PC\subset {\left[\mathrm{0,1}\right]}^2$ differs from the square. In particular, the SG has three-line segments constituting a regular triangle as its border, whereas the border of SAC has the structure of a totally disconnected fat Cantor set. Thus, in contrast to the SG, which has loops at all scales, the SAC is loopless. Consequently, although both patterns have the same similarity dimension $D=\ln 3/\ln 2$, their connectivity dimensions are different. Specifically, the connectivity dimension of the self-avoiding SAC is equal to its topological dimension $d_{\mathcal{l}}\left(SAC\right)=d=1$, whereas the connectivity dimension of the SG is equal to its similarity dimension, that is, $d_{\mathcal{l}}\left(SG\right)=D$. Therefore, the dynamic properties of SG and SAC are also different. Some other noteworthy features of the Sierpiński triangle are also highlighted. Full article

Particular Kottler spacetimes are analytically investigated. The investigated spacetimes are spherically symmetric nonrotating spacetimes endowed with a Schwarzschild solid-angle element. SchwarzschildNairiai spacetimes, Schwarzschild spacetimes with a linear term, and Schwarzschild spacetimes with a linear term and a cosmological constant are studied. The infinite-redshift surfaces are analytically written. To this aim, the parameter spaces of the models are analytically investigated, and the conditions for which the analytical radii are reconducted to the physical horizons are used to set and to constrain the parameter spaces. The coordinate-singularity-avoiding coordinate extensions are newly written. Schwarzschild spacetimes with a linear term and a cosmological constant termare analytically studied, and the new singularity-avoiding coordinate extensions are detailed. The new roles of the linear term and of the cosmological constant term in characterizing the Schwarzschild radius are traced. The generalized Schwarzschild–deSitter case and generalized Schwarzschild–anti-deSitter case are characterized in a different manner. The weak field limit is newly recalled. The embeddings are newly provided. The quantum implementation is newly envisaged. The geometrical objects are newly calculated. As a result, for the Einstein field equations, the presence of quintessence is newly excluded. The Birkhoff theorem is newly proven to be obeyed. Full article

This work focused on the solubility of ethane in three promising ionic liquids {1-Hexyl-3-methylimidazolium bis(trifluormethylsulfonyl) imide [HMIM][Tf2N], 1-Butyl-3-methyl-imidazolium dimethyl-phosphate [BMIM][DMP], and 1-Propyl-3-methylimidazolium bis(trifluoromethyl-sulfonyl)-imide [PMIM][Tf2N]}. The solubilities were measured at 303.15 K to 343.15 K and pressures up to 1.4 MPa using a gravimetric microbalance. The overall ranking of ethane solubility in the ionic liquids from highest to lowest is the following: [HMIM][Tf2N] > [PMIM][Tf2N] > [BMIM][DMP]. The Peng–Robinson equation of state was used to model the experimental data using three different mixing rules: van der Waals one, van der Waals two, and Wong–Sandler mixing rules combined with the Non-Random Two-Liquid model. The average absolute deviations for the three mixing rules for the ionic liquids at the three temperatures were 4.39, 2.45, and 2.45%, respectively. Henry’s Law constants for ethane in [BMIM] [DMP] were the highest (lowest solubility) amongst other ionic liquids studied in this work. The solubility ranking for the 3 ILs was confirmed by calculating their overall polarity parameter (N) using COSMO-RS. The selectivity of CO₂ over C₂H₆ was estimated at three temperatures, and the overall ranking of the selectivity was in the following order: [PMIM][Tf2N] > [BMIM][DMP] > [HMIM][Tf2N] > Selexol. Selexol is an efficient and widely used physical solvent in gas sweetening. It has lower selectivity than the three ionic liquids studied. [PMIM][Tf2N], a promising solvent, has the highest selectivity among the three ILs studied and would, therefore, be the best choice if, in addition to carbon dioxide capture, ethane co-absorption was to be avoided. The enthalpy and entropy of solvation at infinite dilution were also estimated. Full article

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions. Full article

The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint’s coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits $38.65\%$ and $66.24\%$ improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human–robot collaboration experiments. Full article

The continuous Hopfield network (CHN) is a common recurrent neural network. The CHN tool can be used to solve a number of ranking and optimization problems, where the equilibrium states of the ordinary differential equation (ODE) related to the CHN give the solution to any given problem. Because of the non-local characteristic of the “infinite memory” effect, fractional-order (FO) systems have been proved to describe more accurately the behavior of real dynamical systems, compared to the model’s ODE. In this paper, a fractional-order variant of a Hopfield neural network is introduced to solve a Quadratic Knap Sac Problem (QKSP), namely the fractional CHN (FRAC-CHN). Firstly, the system is integrated with the quadratic method for fractional-order equations whose trajectories have shown erratic paths and jumps to other basin attractions. To avoid these drawbacks, a new algorithm for obtaining an equilibrium point for a CHN is introduced in this paper, namely the optimal fractional CHN (OPT-FRAC-CHN). This is a variable time-step method that converges to a good local minima in just a few iterations. Compared with the non-variable time-stepping CHN method, the optimal time-stepping CHN method (OPT-CHN) and the FRAC-CHN method, the OPT-FRAC-CHN method, produce the best local minima for random CHN instances and for the optimal feeding problem. Full article