Search Results (3,480)

This study investigates an IPS-type Metglas/PMN-PT laminated magnetoelectric composite and its feasibility as a reciprocal mechanical magnetoelectric antenna for low-frequency transmission and reception. Finite-element simulations under quasi-static and frequency-domain conditions reveal strong magnetoelectric coupling under an optimal DC bias field, with both the direct magnetoelectric effect (DME) and converse magnetoelectric effect (CME) exhibiting pronounced resonance near 14.5 kHz, governed by the same longitudinal extensional vibration mode. Five IPS samples were fabricated and experimentally characterized. All devices showed resonant frequencies within 14.1–14.5 kHz, peak DME coefficients of 3.0 × 10⁶ to 3.9 × 10⁶ pC/Oe, and peak CME coefficients of 12.0~15.8 Oe·cm/V, confirming good fabrication consistency, transmit–receive reciprocity, and array-integration potential. The parallel IPS antenna generated a magnetic flux density of 37 nT at 1 m, and exhibited an equivalent magnetic noise of 63 fT/Hz^1/2 at 14.45 kHz. These results demonstrate that the proposed IPS structure combines high-sensitivity reception with efficient low-frequency transmission, showing strong potential for miniaturized, low-power, and long-range magnetic communication and underwater communication applications. Full article

Periodic and non-periodic disturbances significantly affect the tracking accuracy of servo systems. A dual-motor drive composite control strategy based on iterative learning feedforward control and super-twisting sliding mode observer is proposed. Initially, a novel reaching law capable of dynamically adjusting gain coefficients based on system states is introduced, leading to the design of a sliding mode controller with proven asymptotic stability. To address non-periodic total disturbances, a super-twisting sliding mode observer is developed, and Lyapunov stability theory is employed to demonstrate system stability and error convergence to zero. A resonant controller is designed to suppress medium- to high-frequency periodic disturbances. For periodic total disturbances, a parameterized feedforward controller based on iterative learning is devised, and an input-shaping filter is introduced to refine the input trajectory. The feedforward control parameters are iteratively updated using a data-driven approach. Experiments are conducted on a differential dual-drive servo system. The nut motor adopts the sliding mode controller with an observer. The screw motor employs the iterative learning feedforward controller. Results show effective suppression of the disturbances. Speed ripple is reduced, and tracking accuracy is significantly improved. The study demonstrates the feasibility and advantage of combining robust control with iterative learning in high-precision servo systems. Full article

Geopolymeric concrete beams are gaining increasing attention as sustainable structural members. The paper presents an experimental investigation on the deflection and cracking behavior of geopolymeric recycled aggregate concrete (GRAC) beams, with emphasis on effects of the longitudinal reinforcement ratio and the recycled aggregate (RA) replacement ratio. Using digital image correlation (DIC) technology, the failure modes, load–deflection curves, deflection characteristics, stiffness, and cracking behavior were systematically analyzed. The results indicated that increasing the reinforcement ratio leads to the same trend in GRAC beams as that observed in ordinary reinforced concrete beams. At 50% RA replacement, GRAC beams exhibit improved cracking resistance, 13.41% higher cracking stiffness, 6.93% lower deflection, and enhanced ductility compared to specimens without RA, attributed to the enhanced RA–matrix interface. However, a further increase in the RA replacement ratio leads to poorer flexural performance of the GRAC beams. In addition, predictive models for cracking moment, stiffness, deflection, and maximum crack width of GRAC beams were proposed based on the experimental results, incorporating the plastic influence coefficient, the comprehensive coefficient for the average strain at the extreme compression zone of concrete and the maximum crack width correction factor. The calculated values agreed well with the test data, offering a basis for structural design and engineering application. Full article

Active control of multi-mode light–matter interactions is crucial for advancing quantum photonic technologies. Although triple-mode plasmon–exciton systems involving two distinct excitonic transitions offer a pathway to multi-level polaritonic states, achieving reversible electrical tuning at room temperature remains challenging. Here, we numerically investigate an electrically tunable triple-mode strong-coupling system comprising a J-aggregate-coated Au@Ag nanorod coupled with monolayer WS₂. The simulated spectra show a UPB–LPB energy separation of approximately 239 meV near the zero-detuning condition. A modest gate voltage (2.0 V to 3.8 V) selectively modulates the middle and lower polariton branches over ∼46 meV, while the upper branch remains largely unaffected. This selective control is elucidated via a triple-mode coupled-oscillator model and Hopfield coefficient analysis, linking the polariton response to the excitonic composition. These results establish a framework for electrically reconfigurable multi-level polaritonic devices, offering potential for ultracompact optical modulators, high-sensitivity multiplexed sensors, and programmable quantum photonic circuits. Full article

The integration of wind energy into power systems relies on forecasting technologies to address operational challenges caused by its volatility and intermittency. This paper proposes a computing architecture for ultra-short-term wind power forecasting. The methodology integrates an adaptive dual-stage signal processing technique with an optimized deep learning model. To manage the non-stationarity of meteorological variables, the Pearson and Maximal Information Coefficient (MIC) analyses are employed for feature selection. The ICEEMDAN algorithm is then used for initial decomposition, followed by sample entropy and K-Means clustering to assess component complexity. Variational Mode Decomposition (VMD) is applied only to the high-frequency component to further separate stochastic fluctuations while preserving relatively stable trend components. A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is constructed to forecast the resulting multi-scale components. To reduce reliance on manual empirical tuning, the Crested Porcupine Optimizer (CPO) is used to fine-tune key network hyperparameters. Evaluations using operational wind-farm data indicate that the developed hybrid method captures the temporal dynamics of wind power and yields lower prediction errors than the tested benchmark models. This research provides a data-driven computing framework for renewable-energy forecasting and related operational analysis. Full article

The tower with an attached vent stack is a special arrangement in chemical tower structures. Flow-induced vibration of this configuration directly affects the safe operation and structural fatigue life of the equipment. This paper investigates the vortex-induced vibration (VIV) characteristics of a two-cylinder system consisting of a tower and its attached vent stack. Through fluid–structure interaction (FSI) simulations of two unequally sized cylinders in a bundled arrangement, the vibration responses under first and second-mode critical wind speeds with a flow direction of 0° are analyzed. The analysis examines lift and drag coefficients, vibration displacements, and wake flow evolution to reveal the vibration response pattern under multi-parameter coupling. When the lift forces obtained from FSI are applied in a static calculation, the static results for both the first and second-mode critical wind speeds are approximately 250% larger than the FSI results, indicating a significant discrepancy. Further analysis shows that in the FSI simulations, a notable phase difference exists between the fluid excitation and the structural response, causing the lift force to do negative work during part of the vibration cycle, thereby limiting the net energy input. Under the second-mode critical wind speed, the lift distribution along the tower height is significantly non-uniform. The conventional static calculation method neglects both the phase difference and the non-uniform lift distribution along the height, leading to overly conservative predictions. Full article

This paper presents an in-depth investigation into the loss-of-excitation (LOE) protection for variable-speed pumped storage units with full-size converters (FSC-VSPSUs). It elucidates the dynamic interaction between electromagnetic transients and converter control loops under various machine-side converter (MSC) control strategies. The study reveals that units employing voltage-oriented control face a fundamental risk of phase-locked loop (PLL) instability following an LOE event. Given the inadequacy of traditional impedance-based protection for FSC-VSPSUs, a novel LOE protection scheme is proposed. This scheme integrates a dynamically tuned main impedance criterion with an acceleration criterion based on the rate of change of the impedance’s real part, incorporating a speed compensation coefficient to accommodate variable-speed operations. Experimental validation on an RTDS platform demonstrates that the proposed strategy accurately identifies LOE faults across various turbine and pumping modes, offering superior response speeds compared to traditional methods and other techniques reported in the existing literature. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Tellurene has a wide bandwidth and low propagation loss at near-infrared wavelengths due to its nonlinear absorption coefficient. Therefore, we prepared tellurene–polyvinyl alcohol (Te-PVA) film as a saturable absorber in an Er-doped fiber laser by liquid phase exfoliation and spin-coating. The modulation depth was 5.25% and the saturation intensity was 17.02 MW/cm. The nonlinear optical properties of the film and its application in high-stability mode-locked operation were studied. A mode-locked pulse with a fundamental frequency of 8.48 MHz and a central wavelength of 1560.10 nm was obtained, with a signal-to-noise ratio which was greater than 75 dB. A traditional soliton mode-locked operation with a pulse width of 1.41 ps was achieved. In addition, eighth- and 19^th-harmonic mode-locked operations were obtained by adjusting the pump power and polarization controller. Our results show that Te-PVA film functioned as a saturable absorber which enabled harmonic mode-locking with an SNR of 75 dB in an Er-doped fiber laser. It is thus an excellent ultra-fast photonics material. Full article

The integration density of photonic integrated circuits is fundamentally limited by evanescent field overlap and subsequent inter-channel crosstalk. Layered transition metal dichalcogenides (TMDCs) bypass these confinement constraints through intrinsic optical birefringence and high refractive indices. Here, we report the near-infrared optical constants and waveguide dispersion of molybdenum diselenide (MoSe₂). Ellipsometry performed on centimeter-scale crystals yields an in-plane refractive index of 4.1–4.7 over 1000–2000 nm, with an extinction coefficient close to the sensitivity limit of the fit away from strong excitonic resonances. To validate the anisotropic dielectric tensor at the device scale, scattering-type scanning near-field optical microscopy (s-SNOM) was utilized to map the propagation of transverse-magnetic modes in 235 nm thick exfoliated flakes. Spatial Fourier analysis of the edge-scattered near-field interference yields effective mode indices that precisely match the modeled dispersion. Using the verified dielectric tensor, finite-element simulations demonstrate that single-mode MoSe₂ waveguides optically outperform equivalent tungsten disulfide (WS₂) benchmarks. The enhanced evanescent field suppression in the claddings of MoSe₂ waveguide increases the coupling length by a factor of 3.5, reducing the required routing pitch and enabling a 12.5% direct increase in on-chip integration density. The results identify MoSe₂ as a high-index anisotropic platform for compact waveguiding in the near-infrared. Full article

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

This paper presents the design, structural optimization, and two-dimensional (2D) technology computer-aided design (TCAD) simulation of a gallium nitride (GaN) vertical Merged P-i-N/Schottky (MPS) diode incorporating a multi-drift-layer doping profile, composite SiO₂/Si₃N₄ passivation, and field-plate (FP) termination. The proposed device is constructed on an n⁺-GaN substrate with a three-sub-layer n-type drift region and a p-GaN/p⁺-GaN anode region. Systematic TCAD simulations are performed to investigate the dependences of key performance metrics—including knee voltage (V_knee), specific on-resistance (R_on), breakdown voltage (BV), reverse leakage current (J_leak), and Baliga’s figure of merit (BFOM)—on the Schottky metal work function, multi-drift-layer doping concentration, drift-layer thickness, Schottky-to-PN contact length ratio (γ_w), operating temperature, and reverse recovery switching transients. Results demonstrate that the MPS architecture effectively decouples forward conduction loss from reverse blocking capability, overcoming the conventional R_on–BV trade-off. The optimal doping profile (n_mm = 2 × 10¹⁵, n_m = 2 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) achieves a BFOM of ~31.97 GW·cm⁻² with BV ≈ 5.98 kV and R_on ≈ 1.12 mΩ·cm². Joint doping–thickness optimization further identifies a graded doping profile (n_mm = 2 × 10¹⁵, n_m = 5 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) combined with layer thicknesses (T_nmm, T_nm, T_n) = (4.49, 5, 20) μm as the overall optimum, achieving BFOM = 55.36 GW·cm⁻² (BV = 6.61 kV, R_on = 0.79 mΩ·cm²)—a +73% improvement, governed by the punch-through/field-stop design principle. The optimal contact ratio of γ_w = 1.33 yields a BFOM of 38.71 GW·cm⁻². Temperature analysis confirms a positive BV temperature coefficient due to drift-region-limited avalanche breakdown, and the BFOM improves monotonically from 33.31 to 37.82 GW·cm⁻² between 200 K and 450 K. Mixed-mode switching simulations show that increasing γ_w substantially reduces reverse recovery charge (Q_rr), demonstrating the strong potential of the proposed MPS diode for high-voltage, high-frequency, and high-temperature power electronic applications. Full article

Accurate customer baseline load (CBL) estimation is crucial for incentive allocation and flexibility potential assessment in demand response (DR) programs. However, residential electricity consumption is highly stochastic, and long-duration DR events often result in missing critical load segments, making it difficult for traditional regression-based and daily load-profile clustering methods to accurately capture the counterfactual baseline pattern. To address this issue, this paper proposes a CBL estimation method that integrates a physics-/domain-informed response-consistency constraint with a conditional generative adversarial network. In the proposed framework, deep soft clustering is employed to extract weekly scale load modes, while mutual information (MI) and autocorrelation coefficient (ACC) are quantified as user-specific conditioning fingerprints to characterize intrinsic consumption behaviors. Comparative experiments on a publicly available real-world dataset demonstrate that the proposed method provides strong event-period accuracy among the recurrent and attention-based benchmark models considered in the main comparison. Under matched response-consistency budgets, PI-ICGAN achieves the lowest constrained DR-period MAE at the tested NRR targets, and the ablation results show that the attention, fingerprint, response-consistency, and GradNorm components contribute to different aspects of the accuracy–consistency trade-off. Full article

Parallel grid-forming energy storage converters based on virtual synchronous generator (VSG) control are prone to active power oscillation and interphase circulating current under load disturbance, unit switching, and parameter mismatch conditions. To address these problems, this paper proposes a dual-layer damping control strategy that combines adaptive virtual damping in the power loop with capacitor current feedback damping in the current loop. First, the small-signal models of the LCL filter, VSG power loop, and parallel converter system are established, and the dominant oscillation modes are analyzed using eigenvalue and participation factor methods. Then, an adaptive damping coefficient is designed according to the active power deviation and frequency dynamic response to suppress low-frequency power oscillation, while a capacitor current feedback branch is introduced to reshape the LCL filter’s resonant poles and attenuate circulating current resonance. Compared with the conventional fixed-damping VSG control, the proposed method reduces active power overshoot and accelerates power redistribution under load step and unit switching conditions. In the traditional control case, the active power peaks of VSG1 and VSG2 reach approximately 30 kW and 40 kW, with an oscillation period of about 1.8 s, whereas the proposed strategy suppresses the oscillatory process and enables the output powers to rapidly reach the preset sharing ratio. In addition, the system frequency can recover to the rated value of 50 Hz without obvious steady-state deviation, and the high-frequency component of the grid-connected current and the interphase circulating current are significantly attenuated. MATLAB/Simulink simulation results verify that the proposed dual-layer damping strategy provides better power oscillation suppression, circulating current mitigation, and frequency dynamic performance than the conventional VSG control. Full article

Fourier Neural Operators (FNOs) exhibit mode saturation on high-contrast inhomogeneous media, and recent multi-scale extensions (MscaleFNO) further worsen out-of-distribution (OOD) generalization. We introduce the Helmholtz Neural Operator (HNO), a physics-informed, FFT-free branch–trunk operator in the DeepONet family, with a hybrid SIREN+learnable-Fourier trunk and a dual-path rank-32 hypernetwork branch, with bounded multiplicative gating on per-mode coefficients. At a matched parameter count (∼1.05 M, five seeds), HNO achieves a 2.6× lower OOD generalization gap than FNO (19.6% vs. 50.6%, $p=1.7\times {10}^{-3}$, Cohen’s $d=5.1$), 5.1× lower than vanilla DeepONet (19.6% vs. 99.9%, $p=8.2\times {10}^{-3}$), and 6.0× lower than MscaleFNO (19.6% vs. 117.4%, $p=2.4\times {10}^{-6}$); MscaleFNO’s deficit grows at 4.2× more parameters, ruling out capacity starvation. HNO is 4.6×/16.4× faster than FNO/MscaleFNO and 64×–245× faster than multi-threaded FD-PML (MKL PARDISO, 12 cores; 183×–698× vs. single-thread scipy.spsolve), making it suitable as a forward surrogate inside many-query workflows. Absolute accuracy on extreme-contrast (15:1) OOD samples is limited (relative $L^2\approx 1$), so HNO is positioned as a many-query surrogate or warm start for refinement loops, not a stand-alone replacement for direct solvers. A scope limitation is that HNO underperforms FNO on elliptic Darcy Flow, confirming specialization for hyperbolic/wave equations rather than universal operator learning. Full article