Search Results (1,000)

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

The growing use of wireless technology significantly increases the exposure of all living organisms to radiofrequency electromagnetic fields (RF-EMF). However, the physiological effects of RF-EMF on plants have not yet been sufficiently researched. In this study, we investigated the effects of RF-EMF radiation in the frequency ranges 1890–1900 MHz (DECT) and 2.4 GHz plus 5 GHz (Wi-Fi) on photosynthetic performance of Tilia plants (Tilia tomentosa). The recorded fast chlorophyll fluorescence transients were used to analyze the structure and function of PSII by the JIP-test. The analysis of the fluorescence of chlorophyll a showed that the RF-EMF interfered with the electron transport processes of photosynthesis. Tilia plants exposed to RF-EMF induced decrease in photosynthetic efficiency (F_V/F_M) and inactivation of part of PSII reaction centers (RC/CS_O). Observations of leaf senescence and lifespan over a period of 102 days showed that RF-EMF-exposed Tilia plants exhibited accelerated aging. Full article

In single-phase PWM rectifiers, due to the inherent time-varying characteristics of the source voltage and current as well as the periodic operation of the converter bridge, the instantaneous input power on the AC side inevitably exhibits a twice-fundamental-frequency pulsation. This phenomenon consequently generates a double-line-frequency (100 Hz) voltage ripple on the DC-link capacitor, which causes an inherent contradiction in conventional voltage outer-loop control between steady-state ripple suppression and dynamic response speed. To address this issue, this paper proposes a control strategy based on an Adaptive Time-Delayed Feedforward Neural Network (Adaptive TD-FNN). The proposed method explicitly introduces the delayed voltage error of half a ripple period into the network state input, thereby achieving time-domain decoupling of the 100 Hz low-frequency disturbance. In addition, a physics-driven training framework is constructed by integrating the rectifier’s discrete difference equation, thereby strengthening the network’s capacity to learn the dynamic characteristics of the system. On this basis, a dynamic adaptive smoothness-weight penalty mechanism is designed to adjust the weighting factor of the current command smoothness constraint in the loss function according to the system operating state. Specifically, the penalty weight is increased under steady-state conditions to suppress command oscillations caused by ripple disturbances, while it is rapidly reduced during load or grid-voltage transients to release the network’s transient optimization capability. Simulation and experimental results show that the proposed Adaptive TD-FNN controller can simultaneously achieve smooth steady-state current command output and fast dynamic voltage regulation without introducing additional complex digital notch-filtering algorithms. Compared with conventional dual-loop control, the proposed strategy reduces the total harmonic distortion (THD) of the grid-side input current from 8.45% to 3.42%, satisfying grid-connected power quality requirements. Meanwhile, under large load transients and grid-voltage disturbance conditions, the DC-link voltage recovery time is about 40 ms, verifying the comprehensive advantages of the proposed method in ripple suppression, dynamic response, and operating-condition adaptability. Full article

Miniature plasma lenses capable of withstanding high laser intensities could provide compact focusing elements for a variety of laser-plasma applications. In particular, they offer a simple route to increase ignitor beam intensity in fast ignition targets while remaining compatible with the geometric constraints of cone-in-target configurations. We report an experimental proof-of-concept demonstration of such a lens using a Ti:Sa femtosecond laser system. The lens is generated by a nanosecond laser pulse incident on a foil aperture, producing an expanding plasma with a transient radial density gradient that focuses a delayed femtosecond pulse. The resulting plasma lens focal spot is reduced to a few microns $⟨D_{\mathrm{FWHM}}⟩\approx 5.5$ $\mathsf{\mu }$m. After accounting for transmitted energy contained within the FWHM contour, the effective intensity enhancement was estimated to be ${\displaystyle \frac{I_{\mathrm{PL}}}{I_0}}\approx 47\pm 15$. Full article

The detector array chips can be used to capture the transient space-time signal of the pulse radiation field. It is mainly composed of a photoelectric array detector and a readout circuit. However, the metal leads used to connect the detector and the readout circuit have long spacing. This can easily introduce additional delays, resulting in a decrease in the response performance of the chip, which cannot meet the goal of simultaneous transmission of ultra-fast detection signals. In recent years, the rapid development of three-dimensional interconnect technology has enabled the chip to achieve shorter interconnect spacing, smaller parasitic parameters and smaller delay time, thereby improving system response performance. The integrated detector array chips composed of three-dimensional interconnects has the advantages of fast signal interconnection transmission speed, high bandwidth, process compatibility and functional expansion compared with the traditional planar architecture. At the same time, there are some limitations and challenges. Therefore, this paper mainly reviews the evolution characteristics of the stacked architecture of the detector array chips, the process development and the nanosecond-level transmission integration challenges. This paper effectively incorporates the three into a unified framework. This provides a solution for the realization of integrated nanosecond detector array chips. Furthermore, it promotes the application and expansion of the chip in the pulse radiation field diagnosis technology. Full article

The dual-active-bridge series resonant converter (DBSRC) is attractive for bidirectional DC conversion, but its output voltage may respond slowly and exhibit overshoot during start-up, load-step, and reference-step transients when conventional controllers are designed mainly from steady-state or small-signal models. This paper addresses the problem of improving the large-signal transient regulation of a DBSRC while avoiding undesired charging and discharging of the switching capacitor and output capacitor. A finite-state-machine-based state-trajectory control method is proposed. Thus, the converter consists of two full-bridge circuits, each with four switches. The proposed technique enhances the dynamic response of output voltage regulation. By examining the system dynamics in two state-plane domains, the switching behavior of the converter can be clearly characterized, enabling an accurate geometric representation of its operating mechanism. Consequently, a finite-state machine controller is designed based on state-trajectory planning. Switching conditions are utilized to achieve fast start-up and step-load transient responses. Finally, experiments are conducted to validate the effectiveness of the proposed control method. Full article

The increasing integration of photovoltaic (PV) systems and nonlinear loads intensifies harmonic distortion and reactive power imbalance in modern power networks. Conventional shunt active power filters (SAPFs) often employ control strategies that perform poorly under uncertain and dynamic grid conditions. This paper develops a hybrid sliding mode control with disturbance observer (SMC+DOB) technique for a PV-integrated SAPF to achieve effective harmonic mitigation, reactive power compensation, and enhanced system robustness. The study models the PV-SAPF system in MATLAB/Simulink (R2025b), where the SMC ensures robust current tracking, while the DOB estimates and suppresses unknown disturbances in real-time. The controller’s performance is evaluated under varying nonlinear and reactive load conditions, as per IEEE 519-2014 standards. Simulation results show that the proposed SMC+DOB scheme reduces total harmonic distortion (THD) by 96.7%—from 31.45% to 1.05%—while maintaining DC-link voltage stability and unity power factor. The integrated control architecture enhances the dynamic performance of SAPF, providing superior harmonic suppression, fast transient recovery, and improved grid stability for PV-connected systems. Full article

Pipelines remain the primary mode of oil and gas transportation but are vulnerable to leaks that pose environmental and safety risks, particularly in two-phase flow systems. Conventional detection methods often struggle under transient multiphase conditions, while many data-driven studies rely on static evaluation metrics that do not reflect continuous monitoring requirements. This study develops a machine learning framework for leak detection using OLGA-simulated datasets from a previously published study, comprising approximately 180,000 labelled samples across nine leak scenarios and one no-leak case. Pressure, temperature, and mass-flow variables were enhanced through feature engineering to capture nonlinear leak behaviour. Random forest and extreme gradient boosting (XGBoost) classifiers were trained using an 80/20 stratified split with synthetic minority oversampling technique (SMOTE)-based balancing applied only to training data. XGBoost achieved 99.2% accuracy and reduced false positives by 53% relative to random forest while maintaining near-zero false negatives. A sliding-window suspicion framework extended static classification into time-dependent detection, producing delays of between 9.81 s and 82.04 s with zero false alarms in the no-leak scenario. Physical validation using pressure, flow, and fast Fourier transform (FFT) analysis confirmed that detections correspond to genuine hydraulic disturbances, demonstrating the reliability and physical credibility of the proposed framework. Full article

Hydrogen is a promising carbon-neutral fuel for future internal combustion engines due to its wide flammability range, high flame speed, and absence of carbon-based emissions. However, its high reactivity significantly increases susceptibility to abnormal combustion phenomena such as knock and pre-ignition, which can compromise engine efficiency, durability, and operational stability. Accurate detection and characterization of knock in hydrogen-fueled spark-ignition engines remain challenging due to the highly transient, broadband, and cycle-dependent nature of abnormal combustion-induced pressure oscillations. Conventional knock indicators based solely on time-domain pressure oscillations or fixed-band frequency analysis are limited in their ability to capture transient resonance behavior and cyclic variability. This study presents an integrated frequency- and time–frequency-domain methodology for knock detection using high-resolution in-cylinder pressure data acquired from a single-cylinder research engine operating under hydrogen port fuel injection (PFI). A discrete Fast Fourier Transform (DFFT) approach applied at stationary points of dynamically windowed pressure signals enables accurate identification of dominant resonance modes while minimizing spectral leakage. A Gaussian-based adaptive windowing strategy is introduced to capture combustion-driven cyclic variations more effectively. Short-Time Fourier Transform (STFT) and sum-based spectral analysis further provide detailed time–frequency localization of transient knock events. The proposed methodology demonstrates a clear separation between normal combustion and knock conditions, enabling reliable cycle-by-cycle identification of abnormal combustion events under varying operating conditions. The experimentally observed resonance frequencies are validated against theoretical predictions using Draper’s acoustic resonance equation, supporting the physical interpretation of knock-induced pressure oscillations. The results demonstrate that the proposed adaptive spectral methodology significantly improves knock detection accuracy compared to conventional indicators and provides a robust framework for advanced knock diagnostics, engine calibration, and combustion control in hydrogen-fueled engines. Full article

To address the voltage regulation problem of the DC-DC buck converter under multi-source disturbances, this paper proposes a composite anti-disturbance control strategy integrating a Chebyshev-based self-evolving fuzzy neural network (SECFNN) and an arctangent super-twisting sliding mode control (ASTSMC). First, to construct the composite anti-disturbance framework, a load algebraic reconstruction compensator (LARC) is utilized to analytically estimate real-time load dynamics, providing active feedforward compensation for extreme load steps. Second, targeting the unmodeled nonlinearities and parameter uncertainties, the SECFNN is deeply integrated into the control loop. It employs a bidirectional structural learning mechanism—dynamically growing and pruning fuzzy rules—to achieve high-precision adaptive approximation and intelligent compensation. Furthermore, serving as the robust inner-loop core of this composite strategy, the ASTSMC is introduced. By replacing the traditional discontinuous sign function with a continuous arctangent operator, it effectively mitigates sliding mode chattering while ensuring the rapid finite-time convergence of the current tracking error. Ultimately, by synergistically fusing feedforward disturbance rejection (LARC), intelligent nonlinear approximation (SECFNN), and robust tracking (ASTSMC), the proposed strategy significantly reduces transient voltage drops and achieves smoother steady-state performance. Comparative simulation experiments demonstrate the superiority of the proposed method, achieving a rapid startup settling time of 6.5 ms, limiting the maximum transient voltage drop to 15 mV, and completing dynamic reference tracking in 1.2 ms. Furthermore, hardware experimental results confirm its practical engineering feasibility, demonstrating a fast startup of 8.3 ms with zero overshoot, effectively mitigating transient voltage drops during load step changes, and completing dynamic tracking in just 2.2 ms, which verifies its reliable dynamic agility and strong robustness under various test conditions. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

We report the synthesis and characterization of the photophysical characterization of a series of rhodamine (Rho)–perylenebisimide (PBI) electron donor–acceptor dyad/triads containing flexible alkyl spacers (ethylene or hexylene chains). Steady-state absorption and emission, femtosecond and nanosecond transient absorption (fs-TA and ns-TA), cyclic voltammetry, triplet–triplet energy transfer (TTET) experiments and DFT/TD-DFT calculations were combined to elucidate the excited-state dynamics. fs-TA spectral study indicates fast decay of the S₁ state and formation of the ³PBI state (0.32–663 ps), which is supported by the ns-TA spectra. The localized PBI triplet (³PBI*) exhibits unusually long lifetimes (up to 272 μs) as determined by the TTET experiment. No long-lived charge-separated (CS) state was observed. While a Förster resonance energy transfer (FRET) probably occurs between PBI and the open-ring rhodamine, a photo-induced electron transfer is proposed to be responsible for the quenching of the fluorescence of the PBI moiety. Full article

Grid-forming (GFM) converters enhance power system stability by emulating synchronous generators, but their limited overcurrent capability under grid faults poses a critical challenge to transient stability. Existing current-limiting methods often force a trade-off between fault current suppression and voltage support. To address this, a direct internal voltage control (DIVC)-based fault current-limiting strategy is proposed. The DIVC framework eliminates inner control loops and directly regulates the internal voltage amplitude and phase by leveraging measurements at the point of common coupling (PCC) and the converter output, enabling fast, accurate current control within a virtual synchronous generator (VSG) architecture. Under mild faults, the strategy prioritizes maintaining the terminal voltage to preserve voltage source behavior; under severe faults, it smoothly transitions to a current-limiting mode that preserves the terminal voltage phase angle to support transient synchronization. The scheme incorporates compensation-enabling criteria, dual-mode amplitude/phase compensation, and power reference modification. Experimental results under an 80% voltage sag demonstrate that the proposed method limits the transient current peak to 1.1 p.u. and ensures oscillation-free recovery within 0.1 s, significantly outperforming conventional current saturation and virtual impedance techniques. The proposed approach also exhibits strong current-limiting capability under unbalanced faults. Full article

Sensorless permanent magnet synchronous motor (PMSM) drives rely on accurate rotor electrical angle and speed estimation, vulnerable to noisy currents, quantization, and sensor biases. Fixed-bandwidth phase-locked loops (PLLs) entail an intrinsic trade-off between fast transient tracking and high-frequency noise rejection. This paper proposes an adaptive PLL based on linear active disturbance rejection control (LADRC), where a virtual coordinate formulation treats electrical-angle mismatch as a lumped disturbance estimated online by a linear extended state observer (LESO). The observer bandwidth dynamically adapts to the LESO innovation. To optimize performance, adaptive-law parameters are tuned offline via success-history adaptive differential evolution with linear population size reduction (L-SHADE). Comparative simulations against a proportional-integral PLL indicate substantially improved robustness to measurement noise, analog-to-digital quantization, and current-sensor DC offset. Specifically, the speed root-mean-square error decreases from $68.9\mathrm{r}/\min$ to $20.7\mathrm{r}/\min$ under $0.15\mathrm{A}$ additive noise, and from $1.55\mathrm{r}/\min$ to $0.48\mathrm{r}/\min$ under 12-bit quantization at $200\mathrm{r}/\min$. These enhancements reduce reliance on high-precision sensing hardware, offering a practical solution for low-cost, highly reliable motor control in complex industrial environments. Full article

This article proposes an intelligent adaptive arbitrary fixed-time sliding mode control (AFxT-SMC) strategy, integrated with an arbitrary fixed-time disturbance observer (AFx-DO), for precise attitude and altitude tracking of quadcopter UAVs. The primary contribution is achieving arbitrary fixed-time convergence of tracking errors and disturbance estimation, allowing designers to freely prescribe any desired settling time, independent of initial conditions and model parameters. In addition, a novel fixed-time reaching law attenuates chattering by driving the discontinuous control component to zero as the sliding surface is approached, while preserving fast fixed-time convergence through adaptive neural network gain tuning. Its coefficients are dynamically tuned by a neural network using backpropagation to handle time-varying dynamics and enhance adaptability. Finally, the arbitrary fixed-time convergence properties of both the proposed arbitrary sliding surface and the AFx-DO are rigorously established through Lyapunov stability analysis. Simulations under external disturbance conditions show that the proposed method outperforms existing adaptive and observer-based controllers in terms of tracking accuracy, transient response, chattering suppression, and energy efficiency. Quantitative analysis results demonstrate that the proposed methodology significantly enhances tracking precision while concurrently reducing control energy expenditure compared to state-of-the-art approaches. Full article