Search Results (4,337)

In the field of engineering, the advancement of automated scoring systems for laboratory reports has been significantly hampered by three persistent challenges: scarcity of high-quality annotated data, high domain-specific complexity, and insufficient model interpretability. To address these limitations, this study proposes an AdaBoost regression model based on multi-level feature engineering and threshold control, denoted as MFTC-ABR. This method constructs a multi-dimensional feature set using a lightweight neural network, which evaluates laboratory reports across four core dimensions: comprehension of experimental principles, completion of experimental procedures, depth of result analysis, and plagiarism detection. At the scoring algorithm level, a dynamic threshold adjustment mechanism is integrated into the AdaBoostReg ensemble learning framework. By redesigning the sample weight update rule, the prediction errors of samples are divided into three intervals: the acceptable region, the stable learning range, and the focus range. Accordingly, a differentiated weight update strategy is implemented, and a history-aware mechanism is introduced to further regulate the attention allocated to individual samples. Finally, experimental results on the power electronics laboratory report dataset show that MFTC-ABR model achieves a mean absolute error (MAE) of 3.09 and a scoring consistency rate of 82% within a five-point error tolerance. These findings validate the effectiveness and practicability of the proposed method for automatic assessment in specialized domains with limited data availability. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Pavement energy harvesting has been investigated as a means of converting traffic loading, solar radiation, and pavement thermal gradients into usable electricity or heat. This paper reviews 135 publications available through March 2026 and evaluates the field from a pavement engineering perspective. The literature is organized into six technology families: piezoelectric systems, mechanical-electromagnetic systems, triboelectric systems, thermoelectric systems, hydronic/geothermal/solar-thermal pavements, and photovoltaic or pavement-integrated photovoltaic-thermal systems. The review considers not only reported energy output, but also structural compatibility, durability, constructability, maintenance requirements, safety, and deployment conditions. The synthesis shows that the most credible near-term roles of piezoelectric and triboelectric systems are self-powered sensing and other localized low-power functions rather than bulk electricity generation. Mechanical-electromagnetic systems can produce larger event-level output, but their practicality is limited to low-speed and highly controlled settings because they rely on deliberate surface displacement. Thermoelectric systems are mechanically compatible with pavements, yet their performance remains constrained by weak and transient temperature gradients. Hydronic and solar-thermal pavements are presently the most infrastructure-compatible option for large-area energy recovery because they deliver useful heat and align with snow-melting, seasonal storage, and adjacent building-energy applications. Photovoltaic and photovoltaic-thermal pavements offer direct electrical generation, but continued challenges with transparent cover layers, surface friction, durability, fouling, and maintenance still limit broad roadway deployment. Overall, the review indicates that future progress will depend less on maximizing peak output in isolated prototypes and more on integrated pavement-energy design, standardized performance reporting, durability assessment, techno-economic evaluation, and corridor-scale demonstration. Full article

The cascaded H-bridge static synchronous compensator (STATCOM) has been widely employed in medium- and high-voltage reactive power compensation applications due to its high modularity, fast response speed, and direct grid connection capability. However, the DC-link voltage exhibits an inherent double-frequency ripple, which poses a serious challenge to power quality. Therefore, numerous Active Power Decoupling (APD) techniques have been proposed. However, existing schemes still exhibit certain limitations: independent APD topologies are associated with higher costs, whereas single bridge-arm multiplexed APD topologies are confronted with issues such as elevated DC-side voltage and increased current stress on the multiplexed arm. Consequently, comprehensive optimization is difficult to achieve in terms of the number of power devices, decoupling accuracy, level of capacitor multiplexing, and device stress. To address the above issues, this paper proposes a DC split capacitor (DC-SC)-based dual bridge-arm multiplexed cascaded H-bridge STATCOM with active power decoupling capability, along with its corresponding control method. By constructing a fundamental-frequency common-mode voltage on the decoupling capacitor, this method effectively suppresses the double-frequency ripple in the DC-side voltage and reduces the current stress on the switching devices. The simulation and experimental results have verified the correctness and effectiveness of the proposed topological structure and control method. Full article

To meet daily grasping needs under lightweight, low-complexity wearable constraints, this study proposes an underactuated multi-finger prosthetic hand with transmission–control co-design to achieve predictable multi-joint synergies and stable grasps under limited actuation. The prototype uses six miniature motors to drive 14 joint degrees of freedom (DOFs): four fingers have active metacarpophalangeal actuation with tendon-driven underactuated proximal and distal interphalangeal joints, while the thumb provides two independently controlled DOFs for opposition expansion and posture adjustment. It supports five-finger power grasps, tripod pinches, and lateral pinches. To mitigate tendon slack and stroke inconsistency, active/passive tendon-length constraints are defined, and an equal-stroke configuration is obtained via chord-to-arc mapping. A layered STM32F767-based controller combines a reference rotation range limit (free motion) with encoder speed-decay detection (contact/near-stall) to realize per-finger termination and overdrive protection without force/tactile sensors. Experiments report a total mass of 176.6 g and a peak single-finger driving force of approximately 2.8 N. Following the Feix GRASP taxonomy (33 types), the hand reproduces 24 types (72.7%), covering power, intermediate and precision grasps, both thumb abduction/adduction postures, and palm–pad–side opposition/contact, with stable grasp formation across objects of varying geometries. Full article

High-precision time and frequency references are essential for satellite navigation, deep-space exploration, and space science missions. To address the large size, high power consumption, and limited integration of conventional Passive Hydrogen Maser (PHM) servo electronics based on discrete analog chains, this paper proposes a miniaturized digital servo architecture for PHMs based on software-defined radio (SDR) and a field-programmable gate array (FPGA). The AD9364 is used as an integrated RF front end for microwave interrogation signal generation, receiver down-conversion, and analog-to-digital conversion (ADC), while digital demodulation, discriminator construction, and closed-loop control are implemented in the FPGA. A dual-frequency interrogation and time-division multiplexing scheme is introduced to separate the atomic and cavity responses, and an oversampling-based processing method combining outlier rejection and averaging decimation is adopted to improve the observation accuracy and noise immunity of weak error signals. Experimental results demonstrate stable closed-loop locking of the atomic transition spectrum, achieving a frequency stability of 1.46 × 10⁻¹² at 1 s, while significantly improving the compactness and integration level of the servo electronics. Full article

Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge. We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks and on-load tap changers, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness ($p<0.001$, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure, which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading. Finally, five ablation studies yield three broader insights. First, structured domain context engineering produces synergistic quality gains that exceed any single knowledge component, demonstrating that prompt composition matters more than the choice of foundational model. Second, even an open source 8B-parameter model outperforms templates given the same prompt, confirming the framework’s backbone-agnostic value. Most importantly, counterfactual faithfulness increases alongside load severity, indicating that post hoc attributions are most reliable in the high-stakes regimes where trustworthy explanations matter most. Full article

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) based on power electronic converters is a promising alternative for testing extreme operating conditions, such as ultra-low speed operation and fault scenarios. However, existing EME control methods suffer from limited system bandwidth and insufficient emulation accuracy, which limits their applicability. To address these issues, this paper proposes an improved current control strategy for the ultra-low speed permanent magnet synchronous motor (PMSM) emulator. First, a mathematical model of the EME based on the topology of the voltage source converter is established. Then, based on the deadbeat control concept, a deadbeat predictive current control (DPCC) strategy is developed to enhance the dynamic performance. Furthermore, to suppress the parameter mismatch disturbance, an optimization scheme based on a cascaded extended state observer (CESO) is introduced. The first-stage ESO is applied to estimate and compensate for total disturbances, while the second-stage ESO is a supplement to suppress the remaining disturbances in the EME system, which improves the robustness of the DPCC controller. Finally, the effectiveness of the improved emulation accuracy of the proposed method is verified through experiments. Full article

There is a growing demand for alternative low cost and sustainable weed management technology suitable for aerobic and organic farming. This study evaluates 915 MHz microwave heating as a potential non-chemical approach for managing weedy rice while assessing its impact on soil physicochemical properties and selected microbial groups. Microwave power levels of 10, 20, and 30 kW were applied to soil at depths of 2.5, 8.9, and 15.2 cm under controlled laboratory conditions. Weed emergence was quantified using the total germinability index (TGI), and soil physicochemical and microbial responses were analyzed in separate experiments. TGI decreased significantly with increasing microwave power and decreasing soil depth, ranging from 0.84 (10 kW at 15.2 cm) to 0 (20 kW at 2.5 cm and 30 kW at 8.9 cm). For 8.9 cm soil depth, energy levels between 176 and 265 kJ/kg resulted in 80–100% emergence suppression, while treatment of 15.2 cm soil at 30 kW for 30 s (188 kJ/kg) reduced TGI by approximately 80% and germination by 64% relative to control. Soil physicochemical properties showed minimal changes, with values remaining within agronomically acceptable ranges. Total bacterial abundance was not significantly affected, whereas ammonia-oxidizing archaea and bacteria were reduced following treatment. These results indicate that microwave heating can effectively suppress weedy rice emergence under controlled conditions, primarily through thermal effects. However, TGI reflects emergence suppression and does not distinguish underlying mechanisms such as lethality, injury, or dormancy. Additionally, limitations including low replication, lack of depth-matched controls, and limited spatial temperature measurements should be considered. Further field-scale studies are needed to validate performance, optimize energy requirements, and assess long-term soil impacts. Full article

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

This research paper presents Volvo SmartCell, an AC battery technology that integrates modular multilevel converters and battery cells to form a unified system for electric vehicle propulsion and power supply. The research work addresses the broader challenge of reducing driveline cost and complexity by replacing traditional components such as inverters, onboard chargers, centralized DC/DC converters, vehicle control units and many more. SmartCell uses distributed Cluster Boards comprised of H-bridges which are controlled via wireless communication to generate AC voltage, deliver redundant low voltage power, and support cell level protection mechanisms. The prototype testing demonstrates that the system can supply traction power by engaging clusters according to the required voltage depending on motor speed, achieve AC grid charging by synthesizing sinusoidal voltages without a dedicated charger, and provide autonomous DC/DC operation through cluster level voltage regulation. Simulations further indicate that multilevel voltage generation can reduce switching losses and improve electric machine efficiency compared to conventional systems. Additional benefits include active cell balancing, support for mixed cell chemistries, and high redundancy through multiple independent power branches. Challenges remain in wireless bandwidth limitations and cost optimization of Cluster Boards. Ongoing development aims to enhance communication robustness and validate safety for non-isolated grid charging. Full article

Vertical axis wind turbines (VAWTs) are promising systems for urban wind energy applications because of their compact layout, omni-directional operation, and favorable integration potential. However, their broader deployment remains limited by poor self-starting capabilities and relatively low aerodynamic efficiency compared to horizontal axis wind turbines. In this study, a passive flow control concept for a straight-bladed VAWT is numerically investigated using a NACA0012 airfoil modified with 45° inclined perforations on the extrados. Four perforated configurations were generated and compared with the baseline profile through a two-stage computational approach. First, steady 2D computational fluid dynamics (CFD) simulations of the isolated airfoils were performed at a free stream velocity of 12 m/s over an angle of attack range of 0–180°. Subsequently, the most relevant aerodynamic trends were assessed at rotor level using transient 2D Moving Mesh simulations for a three-bladed wind turbine with tip speed ratios (TSRs) between 0.5 and 3.5. All perforated variants exhibited higher lift than the baseline airfoil, while the configuration with smaller, denser perforations distributed over the downstream two-thirds of the extrados provided the best overall aerodynamic performance. At TSR = 2.5, this geometry increased the mean moment coefficient from 0.044 to 0.0525 and the power coefficient from 0.109 to 0.131, corresponding to an increase in power output of approximately 20%. These results indicate that inclined extrados perforations constitute a promising passive strategy for improving the aerodynamic performance of small straight-bladed VAWTs, although further 3D and experimental validations are required. Full article

This paper proposes a collaborative energy management strategy based on power prediction and deep reinforcement learning (DRL) to address the trade-offs among economic efficiency, durability, and dynamic performance in fuel cell hybrid power systems (FCHPS) under dynamic driving conditions. First, a hybrid prediction model termed LSTM-LSSVM with Cascade Correction (LSTM-LSSVM-CC) is developed. The cascade correction (CC) mechanism adopts a hierarchical structure to capture both low-frequency steady-state trends and high-frequency dynamic fluctuations, which are typically challenging for single models to represent. By integrating an online residual correction mechanism, this model generates accurate future power demand sequences. Second, a Dynamic Spatio-Temporal Fusion (DSTF) method is introduced to construct a high-dimensional DRL state space. This approach integrates predicted data, historical residuals, and real-time system states, enabling the agent to perform anticipatory decision-making. Third, a Dynamic Hierarchical Adaptive Multi-Objective Optimization Framework (DHAMOF) is designed. This framework dynamically adjusts objective weights and constraint boundaries based on real-time operating characteristics, enabling adaptive switching of optimization priorities across diverse scenarios. Furthermore, a closed-loop control architecture comprising “prediction–decision–execution–feedback” is established. By incorporating rolling horizon optimization and a proportional-integral (PI) residual compensation mechanism, the proposed architecture effectively suppresses prediction error accumulation and mitigates communication delays. Simulation results under combined CLTC-P and WLTP driving cycles demonstrate that, compared to conventional fixed-weight strategies, the proposed method achieves an 11.3% reduction in hydrogen consumption, a 30.9% decrease in $SOC$ fluctuation range, and a 55.3% reduction in power tracking error. Moreover, under disturbance scenarios involving prediction errors, sensor noise, and a 200 ms communication delay, the system exhibits superior robustness: the increase in hydrogen consumption is limited to within 8.3 g/100 km, and the power tracking error is reduced by 65.6% relative to uncorrected baselines. This collaborative optimization approach overcomes the limitations of traditional open-loop prediction and fixed-weight control, offering a novel technical pathway for the high-efficiency and stable operation of fuel cell hybrid power systems. Full article

Under high-power disturbances such as HVDC blocking, stability strategies such as generator tripping are employed to ensure the frequency stability of the sending-end power grid. For renewable energy units, rapid emergency power reduction instead of direct tripping can quickly reduce active power and suppress frequency spikes, while maintaining grid connection to provide dynamic reactive power support, avoiding voltage collapse, and smoothly restoring power after a fault, thus improving the transient stability and resilience of a high-proportion renewable energy grid. However, the control performance of rapid emergency power reduction for wind turbines is limited by the converter’s overcurrent capacity and the unit-side load limit. Sudden large-scale active power reduction can easily cause motor speed fluctuations and mechanical stress accumulation, and may trigger current limiting and protection actions when the inverter current is saturated, or the DC bus voltage exceeds the limit, thus strictly limiting the range and duration of the adjustable power. To address the engineering requirements for rapid active power reduction in wind turbines, this paper proposes a control scheme based on low-voltage ride-through (LVRT) energy dissipation circuit reuse, and simultaneously conducts a special study on LVRT reuse conditions. When the unit receives a command to rapidly reduce active power, the scheme uses a percentage current duty cycle control strategy to drive the energy-consuming circuit to quickly dissipate excess energy. Simultaneously, it controls the pitch angle to increase at the maximum adjustment rate, thus completely eliminating excess power. This scheme leverages the existing LVRT hardware of the wind turbine to expand its functionality without requiring additional equipment. Furthermore, research on LVRT reuse conditions provides crucial support for the reliable operation of the scheme, demonstrating both outstanding economic efficiency and engineering practicality. Full article