Search Results (299)

The electrification of agricultural tractors is a key step toward improving energy efficiency and reducing environmental emissions. However, quantitative evaluation of drivetrain performance remains limited because workload data for electric tractors are scarce, while most available datasets originate from conventional mechanical tractors. In this study, a one-dimensional simulation model was developed to effectively utilize existing workload data by integrating the drivetrain and electrical characteristics of an actual electric tractor. The model combines an electrical subsystem based on field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) with a vehicle subsystem representing the mechanical drivetrain. Model validation was performed through dynamometer experiments using axle torque as input and motor responses as output, showing strong agreement with measured data. The validated model was applied to field-measured workloads to analyze motor performance, battery state-of-charge behavior, usable operating time, and operating points across various agricultural operations. The proposed simulation model enables quantitative evaluation of electric tractor performance under realistic load conditions and can be extended for co-simulation with higher-level control models. In future studies, the model will be utilized as a platform for testing and developing energy-efficient control algorithms for next-generation electric tractor systems. Full article

Seismic metamaterial-inspired periodic foundations have emerged as promising vibration-mitigation concepts capable of attenuating seismic wave propagation within specific frequency bands. This study presents an experimental investigation on the dynamic response of periodic foundation configurations, with and without embedded piezoelectric layers, to evaluate their vibration-attenuation characteristics. The experimental program employed a shake table driven by a 0.75 kW servo motor and included excitation step counts of 3000, 4000, and 5000. Accelerometers mounted on the specimen surfaces recorded vibration data at 80 ms intervals. Three foundation configurations were tested: (i) a conventional reinforced concrete block, (ii) a one-dimensional periodic foundation composed of alternating concrete and rubber layers, and (iii) a periodic foundation incorporating piezoelectric modules. Time-domain and frequency-domain analyses showed that the periodic foundations achieved notable reductions in both peak and RMS accelerations, especially near resonance frequencies. The configuration, including piezoelectric layers, exhibited similar attenuation performance while also generating measurable instantaneous voltage outputs under vibration. However, these voltage peaks—reaching a maximum of 1.64 V—represent only a laboratory-scale, proof-of-concept demonstration of electromechanical coupling rather than a practical or continuous form of energy harvesting, given the inherently sporadic nature of seismic excitation. Overall, the results confirm that the tested system is not a full metamaterial in the classical sense but rather a metamaterial-inspired periodic arrangement capable of inducing band-gap-based vibration attenuation. The inclusion of piezoelectric elements provides auxiliary sensing and micro-energy-generation capabilities, offering a preliminary foundation for future multifunctional seismic-protection concepts. Full article

Renewable energy automatic generation control (AGC) has the characteristics of rapid adjustment and flexibility, which play a critical role in frequency regulation. Abnormal outputs in renewable energy AGC may trigger frequency fluctuations and threaten grid security. To address the above problems in renewable energy, AGC, a combined model-based and data-driven method for determining the single-step allowable action threshold, is proposed. Firstly, an AGC model with multiple frequency-regulating units is built, and the threshold can be obtained through simulation considering system status parameters. Secondly, as the model-based method struggles to satisfy the requirement of rapidity, a data-driven model based on CNN-LSTM is employed to determine the threshold in real-time. The training data is provided by a model-based method. Considering the limited coverage and interpretability of neural networks, a statistical error-prevention method is proposed to avoid deviations. Then, an adaptive piecewise constant approximation algorithm is employezd to reduce threshold update frequency and the burden for dispatchers. Finally, an adaptive threshold adjustment method for extreme scenarios is proposed, ensuring the frequency regulation of renewable energy AGC under extreme scenarios. Through experiments, the reliability and validity of the proposed method in threshold determination and error prevention are validated. Full article

Accurately forecasting air pollutants concentration can reduce health risks and provide an important reference for environmental governance. This study proposes a new deep learning model, GLA-Net, which aims to achieve high-precision prediction of the air quality index (AQI) of monitoring stations. Specifically, GLSTM-Block is designed to use the GAT module to capture dynamic spatial interaction of AQI, generate spatial semantic features, and then use LSTM to capture the temporal correlation characteristics of these spatial characteristics. This paper uses an LSTM network outside of GLSTM-Block to capture the original temporal characteristics of the input data. Then, the temporal characteristics of the LSTM output are added to the dynamic spatiotemporal features of the GLSTM-Block to obtain the final spatiotemporal features as the input of the subsequent temporal attention layer. The temporal attention layer uses a multi-head self-attention mechanism to focus on the impact of the spatiotemporal characteristics of historical air quality data on each prediction time step, and performs AQI prediction through a fully connected layer. Analysis based on measured data from Beijing, Shanghai and Shenzhen shows that the GLA-Net model has significant advantages in predicting single-step and multi-step changes in AQI. The study found that although the model has a large absolute error in predicting concentrations in highly polluted areas, it can better grasp the trend of changes. This feature is particularly evident in Beijing (AQI mean 64.289), with root mean square error (RMSE) of 12.716 and index of agreement (IA) of 0.983. Full article

For the application of a high-performance shortwave infrared (SWIR) detector, a fully integrated analog front-end (AFE) circuit is proposed in this paper, which includes a readout integrated circuit (ROIC) and a 12-bit/100 kHz two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC adopts a direct injection (DI) structure with a pixel size of only 10 µm × 10 µm. The column processing circuit uses a passive correlated double-sampling (CDS) circuit to reduce noise and improve dynamic range. The comparator of four inputs in the ADC solves the problem of linearity reduction caused by charge redistribution during coarse quantization. In addition, the current steering digital-to-analog converter (DAC) is used to compensate for the non-ideal characteristics of the switch, which effectively optimizes the differential nonlinearity (DNL) and integral nonlinearity (INL). The AFE is implemented using SMIC 180 nm 1P6M technology. The post-simulation results show that at a power supply voltage of 3.3 V, the AFE has a frame rate of 100 Hz and a full well capacity (FWC) of 2.8 Me⁻. The linearity can reach 99.59%, and the equivalent output noise is 243 µV. The dynamic range is 73.8 dB. Meanwhile, the signal-to-noise distortion ratio (SNDR) and effective number of bits (ENOB) are 68.38 dB and 11.06 bits, respectively. Full article

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

This study investigates the dynamic characteristics of electro-hydrostatic actuators (EHA), which serve as the core actuating element in vehicle active suspension systems, with the aim of enhancing overall system performance. The purpose of this research is to identify and address the factors limiting EHA dynamic response. Through theoretical analysis from the perspectives of natural frequency properties and power demand, the study reveals that the natural frequency of the motor-pump assembly acts as the primary bottleneck, while insufficient motor output torque represents another major constraint. To overcome these limitations, a method is proposed involving increased maximum motor output torque and reduced rotational inertia of the motor-pump assembly. The feasibility of this approach is validated via frequency domain simulation analysis. Comparative simulations demonstrate that the enhanced EHA system exhibits significantly improved dynamic performance under both step and sinusoidal position commands compared to the baseline system. These findings provide important theoretical insights and practical directions for overcoming actuator performance limitations in vehicle active suspension systems. Full article

This work shows that inverse Judd–Ofelt (JO) analysis of relative absorption spectra, anchored by a single lifetime, provides JO parameters and radiative rates without absolute calibration. The method is applied to Er³⁺, Dy³⁺, and Sm³⁺ in a compositionally identical oxyfluoride glass. Three well-resolved ground-state 4f–4f absorption bands were selected. After baseline removal and wavenumber-domain integration, their normalized strengths S_rel,k (k = 1, 2, 3; k∈S) define a 3 × 3 system solved by non-negative least squares to obtain the anchor-independent ordering (Ω₂:Ω₄:Ω₆). Absolute scaling uses a single lifetime anchor. We report lifetime-scaled Ω_t and A_rad, and the normalized fractions p_k within the selected triplets; as imposed by the method, the anchor-independent ordering (Ω₂:Ω₄:Ω₆) is analyzed, while absolute A_rad and Ω_t scale with τ_ref. The extracted parameters fall within the expected ranges for oxyfluoride hosts and reveal clear ion-specific trends: Ω₂ follows Dy³⁺ > Er³⁺ > Sm³⁺ (site asymmetry/hypersensitive response), while the ordering Ω₄ > Ω₆ holds across all ions (oxide-rich networks). Er³⁺ exhibits the largest Ω₄ and the smallest Ω₆, indicative of pronounced medium-range “rigidity” with suppressed long-range polarizability; Sm³⁺ shows the lowest Ω₂ (more symmetric/less covalent coordination); and Dy³⁺ the highest Ω₂ (strong hypersensitive behavior). Uncertainty was quantified by Monte Carlo resampling of the preprocessing steps, yielding compact 95% confidence intervals; the resulting JO-parameter trends (Ω₂, Ω₄, Ω₆) and normalized f_k fractions reproduce the characteristic spectroscopic behavior known for each ion. This method enables quantitative JO outputs from uncalibrated spectra, allowing direct spectroscopic comparisons and quick screening when only relative absorption data are available. Full article

Existing research has preliminarily achieved stable walking in humanoid robots; however, natural human-like leg motion and adaptive capabilities in dynamic environments remain unattained. This paper proposes a bionic central pattern generator (CPG) gait generation method based on Kimura neurons. The method maps the CPG output to the spatial motion patterns of the robot’s center of mass (CoM) and foot trajectory, modulated by 22 undetermined parameters. To address the vague physical interpretation of CPG parameters, the strong neuronal coupling, and the difficulty of decoupling, this research systematically optimized the CPG parameters by defining an objective function that integrates dynamic balance performance with step constraints, thereby enhancing the naturalness and coordination of gait generation. To further enhance the walking stability of the robot under varying road curvatures, a vestibular reflex mechanism was designed based on the Tegotae theory, enabling real-time posture adjustment during slope walking. To validate the proposed approach, a virtual simulation platform and a physical humanoid robot system were constructed to comparatively evaluate motion performance on flat terrain and slopes with different gradients. The results show that the energy consumption characteristics of robot-coordinated gait are highly consistent with the energy-saving mechanism of human natural motion. In addition, the established reflection mechanism significantly improves the motion stability of the robot in slope transition, and its excellent stability margin and environmental adaptability are verified by simulation and experiment. Full article

This paper presents an enhanced operation strategy for a recently proposed converter called Modified Non-Inverting Step-Down/Up (MNI-SDU) DC-DC converter intended for battery voltage regulation. Unlike the conventional approach, where both switching stages share a single duty cycle, the proposed method controls asynchronously the two duty cycles through a fixed time offset to optimize performance. A methodology is developed to define suitable duty cycle ranges that ensure proper converter operation according to input/output voltage specifications, while simultaneously reducing the current and voltage ripples and electrical stress in the capacitor and semiconductors. Furthermore, a model-based control strategy is proposed, taking into account the enhanced operational characteristics. Consequently, a PI-PI current-mode controller is designed using loop shaping techniques to maintain the output voltage regulated at the desired level. The proposed approach is analyzed mathematically and validated through experimental results. The findings demonstrate that optimizing through asynchronous duty-cycle control with a fixed time offset improves ripple, stress values, and overall efficiency, while maintaining robust output voltage regulation, making this method well-suited for applications requiring compact and reliable power conversion. Full article

Several types of self-balancing vehicles have been successfully developed and commercialized in the past two decades, both as manned vehicles and as autonomous mobile robots. At the same time, due to their characteristic instability and underactuation, a large body of research has been devoted to their control. However, despite this practical and theoretical interest, the current publicly available literature does not cover their systematic design and development. In particular, overall processes that lead to a finished vehicle starting from a set of requirements and specifications have not been examined in the literature. Within this context, this paper contributes a comprehensive mechatronic, dynamics-based procedure for the design of this class of vehicles; to promote clarity of exposition, the procedure is systematically presented using Model-Based Systems Engineering tools and principles. In particular, the proposed design method is developed and formalized starting from an original description of the vehicle, which is treated as a complex system composed of several interconnected multi-domain components that exchange power and logical flows through suitable interfaces. A key focus of this work is the analysis of these exchanges, with the goal of defining a minimal set of quantities that should be necessarily considered to properly design the vehicle. As a salient result, the design process is organized in a logical sequence of steps, each having well-defined inputs and outputs. The procedure is also graphically outlined using standardized formalisms. The design method is shown to cover all the mechanical, electrical, actuation, measurement and control components of the system, and to allow the unified treatment of a large variety of different vehicle variants. The procedure is then applied to a specific case study, with the goal of developing the detailed design of a full-scale vehicle. The main strengths of the proposed approach are then widely highlighted and discussed. Full article

We successfully fabricated micro orthogonal fluxgate sensors using amorphous CoZrNb films. The sensor, measuring 1.5 mm × 0.5 mm, consists of three main parts: the conductor for excitation current flow, the magnetic layer sensitive to an external magnetic field, and the detection coil for measuring output voltage dependent on an external magnetic field. The magnetic layer forms a magnetically closed-circuit in the cross-section, which reduces reluctance and power consumption. Key fabrication challenges, such as poor step coverage and delamination, were effectively addressed by adjusting the sputtering angle, rotating the substrate during deposition, incorporating a Ta adhesion layer, and applying O₂ plasma surface treatment. Optimal sensor performance was achieved by vacuum annealing the CoZrNb films at 300 °C under an applied magnetic field of 500 Oe. This process effectively enhanced magnetic softness and induced magnetic anisotropy, resulting in both very low coercivity (0.1 Oe) and a stable amorphous structure. The effects of operation frequency and the conductor width on the output characteristics of the fabricated sensors were quantitatively investigated. The sensor exhibited a maximum sensitivity of 0.98 mV/Oe (=9.8 V/T). Our results demonstrate that miniaturized orthogonal fluxgate sensors suitable for multi-chip packaging can be applied to measure the Earth’s magnetic field. Full article

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing’s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning. Full article