Search Results (3,524)

To address the issues of insensitivity to high-impedance ground faults and difficulty in identifying reflected wavefronts in single-ended traveling-wave fault location methods for asymmetric ground faults in high-voltage AC transmission lines, this paper proposes a single-ended fault location method based on the modal amplitude ratio and deep learning. First, based on the dispersion characteristics of traveling waves, an approximate formula is derived between the fault distance and the amplitude ratio of the sum of the initial transient voltage traveling-wave 1-mode and 2-mode to 0-mode at the measurement point. Simulation verifies that the fault distance x from the measurement point at the line head is unaffected by transition resistance and fault inception angle, and that a nonlinear positive correlation exists between the distance x and the modal amplitude ratio. The multi-scale wavelet modal maximum ratio of the sum of 1-mode and 2-mode to 0-mode is used to characterize the amplitude ratio. This ratio serves as the input for a Residual Bidirectional Long Short-Term Memory (BiLSTM) network, which is optimized using the Dung Beetle Optimizer (DBO). The DBO-Res-BiLSTM model fits the nonlinear mapping between the fault distance x and the amplitude ratio. Simulation results demonstrate that the proposed method achieves high location accuracy. Furthermore, it remains robust against variations in fault type, location, transition resistance, and inception angle. Full article

Mitral valve orifice area is a key functional metric that depends on complex geometric features, motivating a systematic assessment of the relative influence of these parameters. In this study, the mitral valve geometry is parameterized using twelve geometric variables, and a global sensitivity analysis based on Sobol indices is performed to quantify their relative importance. Because global sensitivity analysis requires many simulations, a Gaussian Process regressor is developed to efficiently predict the orifice area from the geometric inputs. Structural simulations of the mitral valve are carried out in Abaqus, focusing exclusively on the valve mechanics. The predicted distribution of orifice areas obtained from the Gaussian Process shows strong agreement with the ground-truth simulation results, and similar agreement is observed when only the most influential geometric parameters are varied. The analysis identifies a subset of geometric parameters that dominantly govern the mitral valve orifice area and can be reliably extracted from medical imaging modalities such as echocardiography. These findings establish a direct link between echocardiographic measurements and physics-based simulations and provide a framework for patient-specific assessment of mitral valve mechanics, with potential applications in guiding interventional strategies such as MitraClip placement. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article

Mobile robots conducting inspection tasks in coal-mine roadways and operating in complex underground environments are often subjected to demanding conditions such as low adhesion, uneven friction distribution, and localized slippery surfaces. These challenges are significant, predisposing the robots to trajectory deviation and posture instability, thereby presenting substantial obstacles to high-precision tracking control. The primary innovation of this study lies in proposing a hierarchical model predictive control (HMPC) strategy, which addresses the challenges through synergistic, kinematic and dynamic optimization. The core contribution is the construction of dual-layer optimization architecture. The upper-layer kinematic MPC generates the desired linear and angular velocities as reference commands. The lower-layer MPC is designed based on a dynamic model that incorporates ground adhesion characteristics, enabling the online computation of optimal driving forces (F_L, F_R) for the left and right tracks that simultaneously satisfy tracking performance requirements and practical actuation constraints. Simulation results demonstrate that the proposed hierarchical framework significantly outperforms conventional kinematic MPC in terms of steady-state accuracy, response speed, and trajectory smoothness. Experimental validation further confirms that, in environments with low adhesion and localized slippery conditions representative of actual roadways, the proposed method effectively coordinates geometric accuracy with dynamic feasibility. It not only markedly reduces longitudinal and lateral tracking errors but also ensures excellent dynamic stability and reasonable driving force distribution, providing key technical support for reliable operation in complex underground environments. Full article

With the rapid advancement of science and technology, robotics is evolving towards more profound and extensive applications. Nevertheless, the inherent limitations of traditional industrial “caged” robots have significantly impeded the full utilization of their capabilities. Consequently, breaking free from these constraints and realizing human–robot collaboration has emerged as a new developmental trend in the robotics field. The collision-response mechanism, as a crucial safeguard for human–robot collaboration safety, has become a pivotal issue in enhancing the performance of human–robot interaction. To address this, an adaptive admittance control collision-response algorithm is proposed in this paper, grounded in the principle of admittance control. A collision simulation model of the AUBO-i5 collaborative robot is constructed. The effectiveness of the proposed algorithm is verified through simulation experiments focusing on both the end-effector collision and body collision of the robot, and by comparing it with existing admittance control algorithms. Furthermore, a collision-response experimental platform is established based on the AUBO-i5 collaborative robot. Experimental studies on end-effector and body collisions are conducted, providing practical validation of the reliability and utility of the proposed adaptive admittance control collision-response algorithm. Full article

Undergraduate civil engineering students frequently struggle to transition from deterministic to probabilistic reasoning, a conceptual shift essential for modern structural design practice governed by reliability-based codes. This paper presents a design-based research (DBR) contribution and a theoretically grounded pedagogical framework that integrates AI-powered conversational tutoring with interactive simulations to scaffold this transition. The framework synthesizes cognitive load theory, scaffolding principles, self-regulated learning research, and threshold concepts theory. The design incorporates three novel elements: (1) a structured misconception inventory specific to structural reliability, derived from literature and expert elicitation, with each misconception linked to targeted intervention strategies; (2) an integration architecture connecting large language model tutoring with domain-specific simulations, where simulation states inform tutoring and misconception detection triggers targeted activities; and (3) a scaffolded module sequence building systematically from deterministic foundations through probability concepts to reliability analysis methods. Sequential modules progress from uncertainty recognition through Monte Carlo simulation and design applications. We provide technical specifications for the implementation of AI tutoring, including prompt engineering strategies, accuracy safeguards that address known limitations of large language models (LLMs), and protocols for escalation to human instructors. An assessment framework specifies concept inventory items, process measures, and practical competence tasks. Ultimately, this paper provides testable conjectures and identifies conditions under which the framework might fail, structuring subsequent empirical validation with student participants following institutional ethics approval. Full article

The avalanche speed is an important indicator for measuring the intensity of avalanches, and its measurement method is relatively complex. In practical engineering, empirical formulas based on statistics are usually adopted. However, research on the applicability of existing calculation methods in different regions is still insufficient, and further verification and improvement are urgently needed. Based on the integrated space–air–ground field survey data, this study uses RAMMS::AVALANCHE to conduct dynamic numerical simulations of 78 avalanche events in the Qiet’ akesu Gully of the Wenquan to Horgos transportation corridor in the Western Tianshan Mountains during the winter of 2023–2024, analyses the avalanche movement process, and compares the calculation results of the numerical tests of avalanche movement speed with empirical formulas. The results indicate that the velocities calculated using Formula A and Formula B are generally overestimated, approaching approximately 1.5 times the reference value. The mean absolute percentage error of Formula A (19.46%) is lower than that of Formula B (48.27%). In contrast, Formula C exhibits a significantly lower mean absolute percentage error (8.42%) compared with the other two methods, and its results remain stably around one-half of the reference value. Based on these findings, a comprehensive estimation strategy is proposed: twice the value calculated by Formula C is adopted as the primary reference, while two-thirds of the value from Formula A is taken into consideration, and the larger of the two is selected as the final estimated velocity. This strategy ensures the robustness of the results while effectively avoiding the potential overestimation or underestimation associated with reliance on a single empirical formula. This study provides a scientific basis for highway route selection and the placement of avalanche mitigation measures in high-altitude mountainous areas, and offers technical support for the construction and operational safety of infrastructure along the G219 Wenquan–Horgos transportation corridor. Full article

With the rapid growth of the low-altitude economy, ensuring safe unmanned aerial vehicle (UAV) operations over large public events has become a critical issue for urban air mobility. This study proposes a dynamic risk identification and mitigation framework that integrates UAV inherent risk, aerial traffic density, and ground crowd density into a risk evaluation model. To address the absence of real urban air-route data, a simulated low-altitude network was constructed using ArcGIS, K-means clustering, and Delaunay triangulation, while flight paths were optimized through the ripple-spreading algorithm. Based on this model, a risk-aware control mechanism combining rerouting and hovering strategies was implemented to adaptively respond to varying ground risk levels. A total of 412 UAV missions were simulated over a 6.5 h city marathon scenario, followed by an extended evaluation with 1873 missions to assess scalability. The results show that over 20% of UAVs required detouring or hovering under dynamic risk conditions, leading to a 35–50% reduction in high-risk exposure time while maintaining acceptable operational efficiency. The proposed framework demonstrates good adaptability and scalability for risk-aware UAV operations in complex urban environments. Full article

With the advancement of 6G networks, terrestrial centralized network architectures are evolving toward integrated space–air–ground network frameworks, imposing higher requirements on the efficiency of computation offloading and multi-objective collaborative optimization. However, existing single-decision strategies in integrated space–air–ground networks find it difficult to achieve coordinated optimization of delay and load balancing under energy tolerance constraints during task offloading. To address this challenge, this paper integrates communication transmission and computation models to design a two-stage computation offloading model and formulates a multi-objective optimization problem under energy tolerance constraints, with the primary objectives of minimizing overall system delay and improving network load balance. To efficiently solve this constrained optimization problem, a two-stage computation offloading solution based on a Hierarchical Cooperative African Vulture Optimization Algorithm (HC-AVOA) is proposed. In the first stage, the task offloading ratio from ground devices to unmanned aerial vehicles (UAVs) is optimized; in the second stage, the task offloading ratio from UAVs to satellites is optimized. Through a hierarchical cooperative decision-making mechanism, dynamic and efficient task allocation is achieved. Simulation results show that the proposed method consistently maintains energy consumption within tolerance and outperforms PSO, WaOA, ABC, and ESOA, reduces the average delay and improves load imbalance, demonstrating its superiority in multi-objective optimization. Full article

Industrial robotic workstations contribute substantially to the total energy demand of modern manufacturing, yet most existing energy-saving approaches focus on modifying robot trajectories, motion parameters, or the position of the robot’s base. This paper proposes a novel methodology for the automatic optimization of the spatial placement of a fixed technological trajectory within the robot workspace, without altering the task itself. The method combines pre-simulation filtering of infeasible configurations, large-scale energy simulation in ABB RobotStudio, and real measurement using a dual acquisition system consisting of the robot’s controller and an external power meter. A digital twin of the workstation is used to systematically evaluate thousands of candidate positions of a standardized trajectory. Experimental validation on an ABB IRB 1600–10/1.2 confirms a 23.4% difference in total energy consumption between two workspace configurations selected from the simulation study. The non-optimal configuration exhibits higher current draw, greater power variability, and a more intensive warm-up phase, indicating increased mechanical loading arising purely from geometric placement. By providing a scalable, trajectory-preserving approach grounded in digital-twin analysis and IoT-based measurement, this work establishes a data foundation for future AI-driven predictive and adaptive energy optimization in smart manufacturing environments. Full article

High-temperature superconducting (HTS) DC power devices operate at cryogenic temperatures to achieve high power density for aviation applications. Ensuring reliable operation requires an optimized insulation system capable of withstanding cryogenic DC stress. In this study, finite element numerical simulations were conducted to investigate the transient behavior of electric fields in HTS cable insulation under DC stress at cryogenic temperatures. The results demonstrate that the transient field distribution is strongly temperature-dependent, leading to prolonged high-field exposure near ground terminations. Strategies to mitigate electric field enhancement are proposed to improve insulation reliability, supported by a comparative evaluation of various insulating materials. The simulation-based insights provide design guidance for developing resilient insulation systems for HTS and other cryogenic DC devices. Full article

Aiming to address the core issue that the current intelligence evaluation for Unmanned Ground Vehicles (UGVs) overly rely on static performance metrics and lack dynamic quantitative characterization of learning internalization capability (LIC), this study proposes a dynamic evaluation framework based on time series analysis. The framework begins by constructing a multidimensional test scenario parameter system and collecting externally observable performance sequence data. It then introduces a sliding window-based slope-standard deviation collaborative analysis technique to achieve unsupervised division of learning phases, from which five core evaluation metrics are extracted to comprehensively quantify the multidimensional dynamic characteristics of LIC in terms of efficiency, stability, and overall effectiveness. Simulation experiments were carried out using UGVs equipped with three types of path-planning algorithms in low-, medium-, and high-difficulty scenarios. Results demonstrate that the proposed algorithm can effectively distinguish multi-dimensional differences in LIC among different UGVs, exhibiting strong discriminative power and interpretability. This study provides a standardized evaluation tool for UGV intelligent selection, algorithm iteration optimization, and training strategy design, and offering significant reference value for the evaluation of the learnability of autonomous driving systems. Full article

This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system’s initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system’s adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance. Full article