Search Results (2,813)

To address the instability risk of grid-forming inverters under large power dispatch in low-inertia and low-damping power grids caused by renewable energy integration, based on the grid-forming inverter connected to an infinite bus system model, transient stability under power dispatch is conducted. The power dispatch boundaries constrained by transient stability are analyzed by the inverter’s output power-angle characteristics and the equal area criterion. To enable on-demand power dispatch for the grid-forming inverter, a power ramp scheduling strategy constrained by transient stability is proposed. Furthermore, to overcome the limitations of variable-step ramp scheduling, such as a prolonged transient duration, significant output waveform overshoot, and the need for real-time computation, an improved scheme employing virtual inertia emulation is presented, along with its parameter design methodology for the inertia emulation block. The response time and overshoot can be effectively reduced. Finally, simulations and experiments validate the effectiveness of the proposed equivalent-inertia ramp control scheme in improving system transient stability under power dispatch. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-associated liver disease (MetALD), and related fibrosis are increasingly prevalent conditions. The relation of the American Heart Association’s (AHA) cardiovascular health (CVH) metric Life’s Essential 8 (LE8) with MASLD, MetALD, and hepatic fibrosis remains unclear. We aimed to investigate the associations of CVH with MASLD, MetALD, and hepatic fibrosis. Methods: We defined significant hepatic fibrosis as a liver stiffness ≥8.2 kPa measured by vibration-controlled transient elastography. MASLD was defined as steatosis (controlled attenuation parameter of ≥274 dB/m) with ≥1 cardiometabolic risk factor and mild alcohol intake (≤140 g/week [women]; ≤210 g/week [men]). MetALD was defined as steatosis with ≥1 cardiometabolic risk factor and moderate alcohol intake (141–350 g/week [women]; 211–420 g/week [men]). Data from 2962 participants in the Framingham Heart Study (mean age 59 years, 57% women) were used in multivariable-adjusted logistic regression models, accounting for demographic and clinical covariates to relate CVH and liver outcomes. Results: Our study included 2704 participants with mild and 258 with moderate alcohol use. MASLD and MetALD prevalence was 34% and 40%, respectively, and 9% had significant hepatic fibrosis. Each 10-point increase in LE4 score (composite of diet, sleep health, physical activity, and smoking) was associated with 16% lower odds of MASLD (Odds Ratio [OR] 0.84; 95% CI: 0.80–0.90; p < 0.001) but not MetALD. Each 10-point increase in LE8 score was associated with 17% lower odds of hepatic fibrosis (OR 0.83; 95% CI: 0.78–0.89; p < 0.001). Conclusions: Better CVH is related to lower odds of MASLD and significant hepatic fibrosis. Full article

This paper investigates the dynamic behavior of synchronous machines subjected to sudden short circuits. Initially, the case of a single-phase synchronous machine under open-circuit conditions is studied. Analytical derivations of short circuit current expressions are carried out and evaluated using numerical integration methods on a digital computer. The transient responses of both armature and field currents are analyzed, showing their dependence on rotor position and machine parameters. A SIMULINK model is developed to simulate and visualize these responses. Subsequently, the study extends to the case of a line-to-line short circuit in a three-phase synchronous machine with damper windings. The general voltage equations of the three-phase machine are derived and applied to the problem, with numerical integration and SIMULINK simulations confirming analytical insights. Results highlight the key influence of rotor angle, leakage inductance, and damper windings on the dynamics of short circuit currents. Full article

Incipient faults in power cables are difficult to diagnose because their transient signatures are weak, non-stationary, and easily masked by background noise, while labeled real-world samples are often scarce. To address these challenges, this paper proposes an offline diagnosis framework that integrates Whale Optimization Algorithm (WOA)-guided CEEMDAN with a TCN-BiLSTM-Multi-HeadAttention network. The proposed method has three main features. First, WOA is explicitly mapped to the CEEMDAN parameter optimization problem and is used to adaptively optimize the noise amplitude and ensemble number, thereby improving decomposition quality and enhancing weak fault-related components. Second, the optimized intrinsic mode functions are reconstructed into a multi-channel representation that preserves complementary fault information across different frequency bands. Third, a hybrid deep architecture combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and multi-HeadAttention is designed to jointly capture local transient characteristics, bidirectional temporal dependencies, and fault-sensitive feature interactions. Experimental results on both PSCAD/EMTDC simulation data and real-world measured data show that the optimized WOA-CEEMDAN achieves superior decomposition performance, with an RMSE of 0.097 and an SNR of 8.42 dB. On the real-world test dataset, the proposed framework achieves 96.00% accuracy, 97.25% precision, 96.84% recall, an F1-score of 0.970, and an AUC of 0.97, outperforming several representative baseline models. Additional ablation, noise-robustness, small-sample, confusion-matrix, and cross-cable validation results further demonstrate the effectiveness and robustness of the proposed framework for incipient cable fault diagnosis. Full article

Rapid and accurate calculation of lightning-induced electric fields in complex terrain is essential for lightning protection and electromagnetic compatibility analysis. Although conventional full-wave numerical methods such as the finite element method can achieve high-fidelity results, they are computationally expensive and inefficient for large-scale or repetitive engineering analysis. To enable efficient and reliable computation of lightning-induced electrostatic fields over complex terrain, this paper proposes a fast computational framework that integrates multi-level conformal mapping with a multi-objective optimization strategy based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). In the proposed method, irregular terrain boundaries are transformed into analytically tractable domains using multi-level conformal mapping, while the critical mapping parameter is reformulated as a dual-objective optimization problem that simultaneously minimizes the maximum local error and the mean global error. Unlike traditional approaches that rely on empirical tuning or exhaustive traversal of mapping parameters, the proposed framework establishes a closed-loop adaptive optimization process that generates a Pareto-optimal solution set, enabling flexible trade-off selection according to practical accuracy requirements. The method is validated against high-fidelity finite element simulations for representative terrain profiles. The results demonstrate that the proposed approach achieves comparable maximum-error performance while reducing mean error and significantly improving parameter-optimization efficiency relative to exhaustive search methods. The proposed framework provides an adaptive and efficient computational solution for preliminary assessment of lightning-induced electric fields in complex terrain environments, and lays a foundation for future extensions toward more realistic multi-dimensional and transient analyses. The improvements in computational accuracy and efficiency offer significant practical value for rapid lightning protection assessment in large-scale complex terrain engineering, enabling parametric analysis and scheme comparison during the preliminary engineering design stage with sufficient reliability. Full article

In recent years, unmanned aerial vehicles (UAVs) have increasingly become carrying platforms for Earth observation systems equipped with optical, microwave, and other types of sensors, primarily enabling high-resolution observations of above-ground targets. With the development of geophysical methods, bulky instruments originally designed for deep subsurface detection have been progressively miniaturized and made more lightweight, allowing their integration with civilian UAVs and opening new technological avenues for subsurface investigation. We have developed a semi-airborne transient electromagnetic system based on a UAV that is capable of simultaneously obtaining underground resistivity and polarization rate parameters. A survey was conducted over the M’sesa mining area in the Democratic Republic of the Congo. This is a mine pit that has been abandoned for over 50 years and has been flooded to form a lake, making it difficult to detect its deep mineralization potential using traditional ground-based methods. The results clearly delineate the spatial distribution of the Shangoluwe–M’sesa compressional fault and reveal a deep low-resistivity and high-chargeability zone, which provides clues for the exploration of deep deposits. This study will be of significant importance for accelerating the promotion and application of UAV-based semi-airborne electromagnetic exploration technologies. Full article

The dynamic response of the secondary circuit system in nuclear propulsion plants is critical to the power output, safety, and energy efficiency of nuclear-powered ships. High-fidelity thermo-hydraulic simulation models can accurately capture system transients but are computationally expensive and unsuitable for real-time applications. To address this limitation, this study proposes a reduced-order dynamic parameter prediction method that integrates high-fidelity simulation with deep learning. A multi-operating-condition simulation model of a typical nuclear-powered ship secondary circuit system is developed to generate time-series data covering load ramping and propulsion mode switching. Based on this dataset, a conventional recurrent neural network (RNN) and a multilayer long short-term memory (LSTM) network are constructed for multivariate autoregressive prediction of 17 key dynamic parameters, and their performances are systematically compared. Results show that the LSTM significantly outperforms the RNN in capturing long-term temporal dependencies, achieving average RMSE and MAPE values of 0.0228% and 0.365%, respectively. The proposed model completes 50-step-ahead prediction within 0.84 s, satisfying real-time requirements. The hybrid simulation-driven and data-driven framework provides a practical solution for intelligent monitoring and control optimization of nuclear-powered ship propulsion systems. Full article

Classifying power quality disturbances (PQDs) under strong noise conditions remains challenging for existing deep learning models. These models typically separate denoising from feature extraction, often rely on attention mechanisms that operate along only a single dimension, and tend to achieve high accuracy at the cost of high complexity, which limits their performance under low signal-to-noise ratio conditions and hinders practical deployment. To address these limitations, this paper proposes AD-PDAF-Net, which organically integrates three key mechanisms through a co-design strategy. Unlike conventional methods that depend on preprocessing, an adaptive soft thresholding denoising layer is embedded into a lightweight residual network to progressively suppress noise during feature extraction, thereby unifying denoising with feature learning. A parallel dual attention module independently refines features along the channel and temporal dimensions, then adaptively fuses them using learnable weights to capture both frequency domain and temporal characteristics of disturbances. The lightweight network entry replaces aggressive downsampling with small convolutions to preserve transient details, and a bidirectional long short-term memory network (BiLSTM) efficiently captures temporal dependencies. Evaluated on a dataset of 25 disturbance categories defined in IEEE Std 1159-2019, the model achieves a classification accuracy of 97.26% and a Kappa coefficient of 97.02% under 20 dB white Gaussian noise, along with an accuracy of 98.78% under mixed noise conditions. The model has only 0.36 million parameters and a computational cost of just 1.50 GFLOPS. Through this co-design, AD-PDAF-Net achieves both high noise robustness and high classification accuracy with minimal computational overhead, offering an effective solution for time series signal recognition in resource constrained environments. Full article

Background: Discrepancies are frequently observed between liver steatosis grading assessed by conventional B-mode ultrasonography and vibration-controlled transient elastography (VCTE) with controlled attenuation parameter (CAP). This study aimed to identify factors associated with these differences and to evaluate whether the two imaging methods provide comparable steatosis classifications. Methods: We conducted a retrospective cross-sectional observational study including 130 hospitalized patients evaluated over a two-year period who underwent laboratory testing, abdominal ultrasonography, and transient elastography. The analyzed variables included demographic characteristics, nutritional status, comorbidities, and biochemical parameters such as alanine aminotransferase (ALAT), total cholesterol, triglycerides, gamma-glutamyl transferase (GGT), and the fibrosis-4 index (FIB-4). Patients were classified into two groups: concordant steatosis grading between the two methods (n = 61) and discordant results (n = 69). Results: Concordant steatosis grading was more frequently observed in patients with serum total cholesterol > 200 mg/dL (45.9%) and FIB-4 values between 1.45–3.25 (44.2%). A trend toward higher concordance was also observed in patients with elevated triglycerides. In contrast, viral liver disease was significantly associated with discordant results (26.2%). Higher fibrosis stages assessed by VCTE (F ≥ 2) and FIB-4 values > 3.25 showed a non-significant trend toward discordance. Conclusions: Several clinical and biochemical factors influence the agreement between ultrasound and VCTE-based CAP in the assessment of hepatic steatosis. Elevated cholesterol and intermediate FIB-4 values were associated with concordant results, whereas viral liver disease was associated with discordance between the two imaging modalities. Full article

Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance with increasing movement variability and speed. To fill this gap, we developed a method based on generalized eigenvalue decomposition (GED) to identify and suppress highly variable, non-periodic—especially transient—artefacts due to very rapid, free full body movements of different types, as they occur during sports practice. By leveraging the contrast between covariance matrices of artefactual and resting-state EEG segments, this approach isolates motion-related components for removal during multichannel EEG signal reconstruction. The method was validated on two ecological datasets featuring stereotyped head and body movements and dynamic table tennis. Comparison with state-of-the-art technique showed superior performance of our method in terms of signal-to-error ratio (SER), artefact-to-residue ratio (ARR), brain spectral power preservation and computation time. Sensitivity analysis was applied to demonstrate the method’s robustness to parameter changes. These findings highlight the potential of the proposed method as a robust, generalizable approach for motion artefact suppression in mobile EEG, particularly when applied in extreme recording conditions like during active sports activity. Full article

To investigate the influence of train operating speed on the transient characteristics of the pantograph–catenary arc, this paper establishes an integrated simulation model encompassing the traction network, electric locomotive, and arc. In this model, the traction network adopts a chain circuit model based on multi-conductor transmission line theory. The electric locomotive model considers the train body and the on-board transformer. For the pantograph–catenary offline arc, an improved Habedank model is employed, which takes the train operating speed and arc current as variables. Based on this model, this paper systematically investigates the variation patterns of arc electrical parameters and transient currents in each line of the traction network with train operating speed under pantograph–catenary offline. The simulation results indicate that as train speed increases, both the steady-state arc voltage and the maximum voltage at arc ignition rise, and the arc extinction time at current zero-crossing is prolonged. The peak arc currents on the contact wire, feeder, protective wire, and rails decrease, while the transient current on the ground wire increases. This study can provide a reference for the electromagnetic compatibility design, insulation coordination optimization, and electromagnetic protection of high-speed railway traction power supply systems. Full article

Friction pairs in wet clutches operate under complex conditions, which can cause surface damage and reduce overall clutch reliability. Surface texturing is an established technique for improving the tribological performance of such mechanical interfaces. Inspired by the wet adhesion properties of tree frog foot pads, a bionic regular hexagonal micro-texture was designed on the mating steel plate. A three-dimensional transient computational fluid dynamics (CFD) numerical methodology was developed and rigorously verified via pin-on-disc friction experiments. Subsequently, this verified numerical framework was extrapolated to establish disc-on-disc CFD models. The results demonstrated that the bionic hexagonal micro-texture altered flow field characteristics, increasing the local maximum flow velocity by 7.9% compared to untextured surfaces. Furthermore, the micro-textured grooves expanded the effective area for convective heat transfer and facilitated local fluid exchange, reducing the maximum average bulk temperature by 20.5% and the maximum radial temperature by 20.7%. Adjusting the structural parameters of these micro-textures further regulated the interfacial flow and temperature fields; notably, deeper grooves induced vortices at land region edges, accelerating flow velocity and decreasing the overall radial temperature gradient. This study provides a theoretical reference for enhancing the thermo-hydrodynamic performance of wet clutch friction pairs. Full article

Accurate prediction of transient melt pool thermal fields in Laser Powder Bed Fusion (LPBF) is essential for understanding melt pool geometry and defect formation mechanisms, yet conventional finite element methods (FEM) impose prohibitive computational costs for parametric process exploration. A variational physics-informed neural network (VPINN) framework is presented for 3D transient thermal modeling of a GH3536 single-track LPBF scan. The framework incorporates a continuously differentiable Goldak double-ellipsoid moving heat source, temperature-dependent thermophysical property surrogates, and an effective heat-capacity treatment of latent heat associated with solid–liquid phase change and vaporization. These components are embedded in a weak-form residual-minimization scheme with octree-adaptive domain decomposition, hierarchical Legendre test functions, and sequential sliding-window time marching. Effective absorptivity is inferred jointly with the network parameters, using sparse experimental melt pool profiles as supervision. Within a parametric study covering laser powers from 100 to 140 W and scan speeds from 1000 to 1500 mm/s, the predicted melt pool width, depth, and aspect ratio agree closely with FEM benchmarks and cross-sectional optical micrograph measurements across both supervised and held-out interpolation conditions, with total relative L₂ nodal temperature errors ranging from 3.23% to 6.75%. Following a one-time offline training investment of 15,323 s that simultaneously resolves the full parametric space, surrogate inference reduces per-condition query time from 3000–4000 s (FEM) to merely 4–5 s, delivering a speedup of two to three orders of magnitude and making the framework increasingly cost-effective for high-throughput parametric studies and digital-twin integration as the number of queried conditions grows. Full article

Precise curvilinear path tracking remains a persistent challenge for autonomous agricultural machines, where conventional Model Predictive Control (MPC) suffers from poor adaptability to varying curvatures and high computational overhead in unstructured farmland environments. This paper proposes a soft-constrained MPC framework enhanced by a two-layer fuzzy architecture and Recursive Least Squares filtering to address these limitations simultaneously. The first fuzzy layer dynamically adjusts the MPC prediction horizon in response to real-time path curvature, enabling proactive steering on complex curved trajectories. The second fuzzy layer tunes the state weighting matrix online based on lateral and heading deviations, improving transient tracking accuracy without increasing computational cost. Recursive Least Squares filtering is further integrated to suppress sensor noise and compensate for tire slip dynamics inherent to farmland operation. The proposed framework is validated using MATLAB simulations on both constant-curvature semicircular paths and variable-curvature S-curve trajectories at operational speeds of 2.0 and 2.5 m/s, followed by outdoor field trials on a scaled autonomous robot platform. Simulation results demonstrate average tracking error reductions of 52.7–55.9% on constant-curvature paths and 10.8–18.2% on variable-curvature paths compared to fixed-parameter soft-constrained MPC. Field experiments confirm practical viability, achieving an RMS lateral error of 0.131 m over a 50 m curved route on natural terrain. These results demonstrate that the hierarchical decomposition of adaptation objectives yields substantial accuracy gains while preserving real-time feasibility on resource-constrained embedded platforms. Full article

To address the strongly nonlinear hydrodynamic coupling and complex maneuvering challenges encountered by large ships during berthing operations in restricted waters, this paper proposes a high-precision autonomous berthing control system incorporating four-quadrant propeller hydrodynamics. Based on an improved Mathematical Maneuvering Group (MMG) framework, a three-degree-of-freedom (3-DOF) dynamic model is established to accurately capture the transient thrust and torque mappings of the propeller over all four quadrants. A dynamic line-of-sight (LOS) guidance system with a nonlinearly decaying acceptance radius is tightly coupled with PD/PI controllers to coordinate and regulate the rudder angle and propeller rotational speed. The numerical solver was rigorously validated against turning-test data for the S-175 container ship, with the errors of the key parameters all controlled within 15%. Subsequently, under the environmental conditions of Yangshan Port, full-condition path-planning and berthing simulations were conducted for the novel B-573 container ship under steady-current disturbances with multiple intensity levels (0 to 1.5 m/s) and multiple flow directions. Quantitative evaluation shows that, under the highly challenging current condition of 1.0 m/s, the dynamic corrective mechanism effectively drives the global mean absolute error (MAE) to converge to 85.50 m, representing a 62% statistical reduction relative to the transient peak value. In addition, a parameter sensitivity analysis based on the cumulative cross-track error confirms that, when subject to variations in the underlying hydrodynamic parameters, the proposed system can suppress fluctuations in trajectory error to a very low level, thereby demonstrating a certain degree of control robustness. During the terminal berthing stage, the vessel smoothly completed an extreme deceleration from an initial speed of 6.4 m/s to a full stop within 588 s, while constraining the maximum astern rotational speed to −2 rps and seamlessly passing through all four propeller quadrants. The results confirm that the proposed autopilot framework possesses a certain degree of engineering feasibility in complex maritime environments. Full article