Search Results (7,872)

With the expansion of interconnection in power systems and the extensive adoption of phasor measurement units (PMUs), the secure operation of power systems has been increasingly covered in research. In this article, a unified online framework for pre-fault and post-fault dynamic security assessment (DSA) is proposed. First, maximum mutual information (MIC) and the random subspace method (RSM) are employed to select the key variables and enhance the diversity of input data, serving as feature engineering. Then, a deep forest (DF) regressor and classifier are utilized respectively to predict security margin (SM) and security state (SS) during online pre-fault and post-fault DSA based on the selected variables. In pre-fault DSA, scenarios with high SM are identified as stable, while those with low SM are forwarded to post-fault DSA. In addition, a time self-adaptive scheme is employed to balance low response time and high prediction accuracy. This approach prevents the misclassification of unstable scenarios as stable by either outputting high-credibility predictions of unstable SS or deferring decisions on SS until the end of the decision-making period. The unified framework, tested on an IEEE 39-bus system and a practical 1648-bus system provided by the PSS/E version 35 software, demonstrates significantly improved assessment accuracy and response times. Specifically, it achieves an average response time (ART) of 2.66 cycles for the IEEE 39-bus system and 3.13 cycles for the 1648-bus system while maintaining an accuracy exceeding 98%, surpassing the performance of currently widely used deep learning models. Full article

Modern, electrically operated heat pumps are characterized by a high degree of efficiency and represent an attractive alternative to conventional heating systems. However, the noise emissions from heat pumps installed outside can lead to increasing noise pollution in densely populated residential areas, which represents an obstacle to widespread use. As part of a research project, a heat pump mock-up was built based on an outdoor unit in the Fraunhofer IBP. With this mock-up, investigations have now been carried out with a prototypical silencer–resonator concept. The aim was to reduce the sound power on the outlet side of the heat pump mock-up. To estimate the effect of this silencer–resonator concept for heat pumps, FEM simulations were first carried out using COMSOL Multiphysics^® with a simplified model. The simulation results validated the silencer–resonator concept for heat pumps and indicated the considerable potential for sound reduction. A measurement was then set up, with which different silencer lengths and absorber thicknesses in the silencer were tested. The measured sound attenuation was higher than the simulated values. The results showed that porous absorbers with sufficient thickness can achieve effective performance in the mid-frequency range. A maximum sound power reduction of 5.7 dB was achieved with the 0.15 m absorber. Additionally, Helmholtz resonators were implemented to attenuate the low-frequency range and tonal peaks. With these resonators sound attenuation was increased to 7.7 dB. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

This article studies different stator structures and modeling methods for using grain-oriented electrical steels (GOES) to improve the performance of high-power-density permanent magnet synchronous motors (PMSMs). The magnetic characteristics of GOES samples are measured under magnetizations at different angles and frequencies. Models of various GOES stator teeth and yokes are established. The effects of different GOES stators on PMSM performance are studied, and their advantages and disadvantages are compared. Three typical GOES PMSM prototypes are fabricated and tested to demonstrate the superiority of GOES stators and validate the effectiveness of the established models. Full article

This study aimed to investigate the efficacy of a 12-week blood flow restriction (BFR) resistance training (BFRRT) program in enhancing anaerobic power. Changes in anaerobic power were compared following 12 weeks of resistance training using three approaches: low-load resistance training with BFRRT at 30% of one-repetition maximum (1RM), traditional high-load resistance training (HRT) at 80% of 1RM, and traditional low-load resistance training (LRT) at 30% of 1RM. Twenty-one male college students were randomly assigned to the BFRRT (n = 7), HRT (n = 7), or LRT (n = 7) groups. The BFR for BFRRT was applied to the proximal femur at 100–130 mmHg. Each group exercised three times per week for 12 weeks. Anaerobic power and metabolic fatigue levels were evaluated using the Wingate Anaerobic Test (WAnT) every 3 weeks, with blood lactate concentrations measured before and after each session. Outcomes included peak power, mean power, fatigue rate, and time to peak power, analyzed via two-way mixed-model analysis of variance. The results revealed a significant group × time interaction for anaerobic peak power, with the blood flow restriction training group demonstrating earlier improvements compared with traditional high-load resistance training, while no significant between-group differences were observed for mean power. Post hoc analysis revealed that BFRRT improved peak power by Week 6, HRT by Week 9, and LRT showed no improvements. BFRRT significantly enhanced anaerobic power in a shorter duration compared with HRT, despite utilizing lower loads and normal-speed exercises. These findings suggest that BFRRT is an effective method for improving anaerobic power while utilizing lower external loads than HRT. Full article

This study aimed to establish the concurrent validity and agreement of the K-power (KINVENT Biomecanique, Montpellier, France) hybrid sensor system that combines Ultra-Wideband and Inertial Measurement Unit measures against criterion timing gates for recording 20-m sprint performance in adolescent athletes. Fifteen trained adolescent track and field sprinters (age: 15.2 ± 2.4 years) performed two maximal 20-m sprints. Sprint times were simultaneously recorded using timing gates and the K-power sensor. Validity and agreement were assessed using paired-samples t-tests, Intraclass Correlation Coefficients (ICCs), Coefficient of Variation (CV), and Bland–Altman analysis. Sensitivity was determined by comparing the Typical Error (TE) to the Smallest Worthwhile Change (SWC). No significant systematic bias was observed between the devices (p > 0.05). The K-power sensor demonstrated excellent absolute agreement (ICC = 0.96, [95% CI = 0.94–0.98) and a low relative error (CV = 1.07%). The device displayed high sensitivity, with a TE (0.034 s) smaller than SWC (0.040 s). In conclusion, the K-power sensor is a valid and reliable instrument for measuring 20-m sprint times, being a practical alternative to timing gates. While the system is sensitive (TE < SWC), the Minimal Detectable Change of 0.094 s likely reflects the inherent biological variability of adolescent mechanics; thus, coaches should view changes exceeding 0.09 s as meaningful for individual athletes. Full article

This study investigates the mechanical response of cemented silt subjected to 28 days of curing by integrating two predictive methodologies: porosity–cement index (η/C_iv) and machine learning (ML) models. The soil was compacted over a wide range of molding water contents and dry densities, including optimum and off-optimum states, and stabilized with varying cement contents. Unconfined compressive strength ($q_u$) and splitting tensile strength ($q_t$) were evaluated as functions of cement dosage, curing time, porosity, water content, and the specific gravities of the soil and cement. The η/C_iv index demonstrated a strong predictive capability for both $q_u$ and $q_t$, with determination coefficients exceeding 0.980, and exhibited the expected power-law decay with increasing η/C_iv. ML algorithms—particularly Gaussian Process Regression with a Matern 5/2 kernel—outperformed the empirical model, achieving R² values of 0.963 (validation) and 0.997 (testing) for $q_u$ prediction. The $q_t$ model similarly reached R² = 0.984–0.988, demonstrating high generalization and stability across curing and compaction conditions. Experimental results revealed substantial strength gains with decreasing η/C_iv, with $q_u$ increasing from 100 kPa at η/C_iv = 46 to 2900 kPa at η/C_iv = 19, while $q_t$ rose from 10–15 kPa to 300 kPa across the same range. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. Full article

Perfluorosulfonic acid (PFSA) membranes suffer from severe conductivity decay caused by dehydration at elevated temperatures, hindering their application in medium- to high-temperature proton exchange membrane fuel cells (MHT-PEMFCs). To address this, Nafion/SiO₂ composite membranes with systematically varied filler contents were fabricated via a sol–gel-assisted casting strategy to enhance hydration stability and proton transport. Spectroscopic and microscopic analyses reveal a homogeneous nanoscale dispersion of SiO₂ within the Nafion matrix, along with strong interfacial hydrogen bonding between SiO₂ and sulfonic acid groups. These interactions effectively suppress polymer crystallinity and stabilize hydrated ionic domains. Thermogravimetric analysis confirms markedly improved water retention in the composite membranes at intermediate temperatures. Proton conductivity measurements at 50% relative humidity (RH) identify the Nafion/SiO₂-3 membrane as exhibiting optimal transport behavior, delivering the highest conductivity of 61.9 mS·cm⁻¹ at 120 °C and significantly improved conductivity retention compared to Nafion 117. Furthermore, single-cell tests under MHT-PEMFC conditions (120 °C, 50% RH) demonstrate the practical efficacy of these membrane-level enhancements, with the Nafion/SiO₂-3 membrane exhibiting an open-circuit voltage and peak power density 11.2% and 8.9% higher, respectively, than those of pristine Nafion under identical MEA fabrication and operating conditions. This study elucidates a clear structure–property–transport relationship in SiO₂-reinforced PFSA membranes, demonstrating that controlled inorganic incorporation is a robust strategy for extending the operational temperature window of PFSA-based proton exchange membranes toward device-level applications. Full article

The tool-point frequency response function (FRF) of a spindle–tool system plays a crucial role in predicting machining stability. Among the factors influencing the FRF, the interface characteristics between the spindle and the tool holder are particularly significant, especially when different holder designs are used. This study focused on identifying these interface characteristics for two common tool holder types—BT and BBT—to improve FRF prediction accuracy. The receptance coupling substructure analysis (RCSA) method was employed in conjunction with finite element modeling (FEM) to characterize the spindle–tool holder interfaces without needing extensive experimental tapping tests. Finite element models were developed to generate receptance components for various tool holder–tool assemblies, enabling efficient and accurate coupling within the RCSA framework. The identified interface parameters were applied to predict the tool-point FRFs of the cutter clamped in a BT tool holder with different overhang lengths. The predicted and measured tool compliances differed by 3–4.6%, demonstrating high agreement and reliability. The proposed methodology provides a powerful tool for predictive modeling of dynamic behavior in spindle–tool systems under varying tooling conditions, enhancing process planning and evaluation of the cutting stability in high-precision machining. Full article

Crankshafts are among the most important parts of modern internal combustion engines. Owing to the power transmission demand, sufficiently high strength is usually necessary for the application of the component. In this paper, a new crankshaft bending experimental method was proposed to shorten the corresponding test. A modified particle filtering algorithm approach was proposed for predicting the remaining fatigue life of a crankshaft during bending fatigue experiments. The predicted fatigue life was used to replace the actual experimental results for further analysis if the accuracy requirements were fulfilled; in this way, the experimental duration was obviously shortened. The main conclusion drawn from the research is that, compared with the traditional particle filtering algorithm approach, the modified particle algorithm approach proposed in this paper can more accurately predict the remaining fatigue life of a crankshaft using less experimental data, which makes it possible to circumvent actual bending fatigue experiments of crankshafts in providing theoretical guidance for the design process. Full article

High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the influence of the relationship between coupling capacitance and input capacitance on monitoring results, an RC-type signal input circuit with enhanced adaptability has been designed for practical engineering scenarios that may involve large input capacitance. Furthermore, a mixed-signal measurement method based on phase dithering is proposed to eliminate detection errors caused by relative phase drift during synchronous sampling in existing signal injection approaches. This improvement enhances measurement accuracy and offers a more robust theoretical basis for selecting injection signal frequencies. The hardware circuit architecture and data processing scheme presented in this work are straightforward and have been validated using an experimental prototype tested at 50 Hz/500 V and 2000 Hz/300 V. Long-term energized testing demonstrates that the system operates stably at room temperature with a relative measurement error below 0.5%. This study provides a high-precision, easily implementable non-contact measurement solution for online monitoring of low-frequency, low-voltage signals in complex electromagnetic environments such as industrial control signals, low-voltage power signals, and rail transit signals. Full article

The collision dynamics of binary seawater droplets are pivotal in marine engineering applications, like spray desalination and engine cooling. While high-fidelity simulations can resolve these dynamics, they are computationally prohibitive for rapid design and analysis. This study introduces the first interpretable machine learning (ML) framework to predict and elucidate the collision outcomes of head-on binary seawater droplets. A high-fidelity numerical dataset, generated via Modified Coupled Level Set-VOF (M-CLSVOF) simulations across a broad Weber number (We) range, serves as the foundation for training multiple classifiers. Among the tested algorithms, the Random Forest model achieved superior performance with 96.2% accuracy. The model’s predictions precisely identified the critical Weber number for the transition from coalescence to reflexive separation at We ≈ 22.3 for seawater. Moving beyond black-box prediction, we employed SHapley Additive exPlanations (SHAP) to quantitatively deconstruct the model’s decision-making process. SHAP analysis confirmed the dominance of the Weber number (75% contribution) and revealed the context-dependent role of the Reynolds number (25% contribution) in modulating the collision outcome. Furthermore, a comparative analysis with freshwater droplets quantified a 6% elevation in the critical Weber number for seawater, attributed to salinity-induced modifications in fluid properties. Finally, a machine-learned regime map in the We-Ohnesorge space was constructed, delineating the coalescence and separation boundaries. This work establishes ML as a powerful, interpretable surrogate model that not only delivers rapid, accurate predictions but also extracts fundamental physical insights, offering a valuable paradigm for optimizing marine spray systems. Full article

The electric propulsion system is a critical determinant of unmanned aerial vehicles’ (UAVs’) operational capabilities, particularly endurance performance. This paper proposes a high-precision modeling framework for UAV electric propulsion systems to improve endurance estimation. By integrating dimensional analysis based on the Buckingham $\pi$ theorem with data-driven parameter fitting, the method accurately predicts propeller thrust, power, and motor current under varying inflow conditions using limited experimental data. The proposed models and complete implementation are publicly available, facilitating reproducibility and further research. The key novelty of this work lies in the tight integration of dimensional analysis (via Buckingham’s $\pi$ theorem) with a data-driven torque-based motor current model, enabling accurate cross-configuration predictions for both propeller aerodynamics and motor electrical characteristics using limited experimental data. The model is rigorously validated against the UIUC propeller database, a custom-built inflow test rig, and actual flight tests. The results demonstrate that the proposed approach achieves superior prediction accuracy across multiple propeller-motor configurations while significantly reducing computational costs. This work provides a reliable foundation for improving UAV endurance estimation and propulsion system design. Full article

Background: Perceptual decision-making requires transforming sensory inputs into goal-directed actions under uncertainty. Neural oscillations in the theta band (3–7 Hz), particularly within frontal regions, have been implicated in cognitive control and decision confidence. However, whether changes in theta oscillations reflect greater effort during ambiguous decisions or more efficient control during clear conditions remains debated, and theta’s relationship to stimulus clarity is incompletely understood. Purpose: This study’s purpose was to examine how task difficulty modulates theta activity and how theta dynamics evolve across the decision-making process using two complementary analytical approaches. Methods: Electroencephalography (EEG) data were acquired from 26 healthy adults performing a face/house categorization task with images containing three levels of scrambled phase and Gaussian noise: clear (0%), moderate (40%), and high (55%). Theta dynamics were assessed from current source density (CSD) time courses of event-related potentials (ERPs) and single-trials. Statistical comparisons used Wilcoxon signed-rank tests with false discovery rate (FDR) correction for multiple comparisons. Results: Frontal theta power was greater for clear than noisy face stimuli (corrected p < 0.001), suggesting that theta activity reflects cognitive control effectiveness and decision confidence rather than processing difficulty. Connectivity decomposition revealed that frontoparietal theta coupling was modulated by stimulus clarity through both phase-locked (evoked: corrected p = 0.0085, dz = −0.61) and ongoing (induced: corrected p = 0.049, dz = −0.36) synchronization, with phase-locked coordination dominating the effect and showing opposite directionality to the induced components. Conclusions: Theta oscillations support perceptual decision-making through stimulus clarity modulation of both phase-locked and ongoing synchronization, with evoked component dominating. These findings underscore the importance of methodological choices in EEG-based connectivity research, as different analytical approaches capture different aspects of the same neural dynamics. The pattern of stronger theta activity for clear stimuli is consistent with neural processes related to decision confidence, though confidence was not measured behaviorally. Full article