Search Results (1,209)

The development of flexible and self-powered electronic systems requires triboelectric materials that combine high charge retention, mechanical compliance, and stable dielectric properties. Here, we report a redox reaction approach to prepare liquid metal particle-reduced graphene oxide (LMP@rGO) core–shell structures and introduce into a poly(acrylic acid) (PAA) hydrogel to create a high-performance triboelectric layer. The spontaneous interfacial reaction between gallium oxide of LMP and graphene oxide produces a conformal rGO shell while simultaneously removing the native insulating oxide layer onto the LMP surface, resulting in enhanced colloidal stability and a controllable semiconductive bandgap of 2.7 (0.01 wt%), 2.9 (0.005 wt%) and 3.2 eV (0.001 wt%). Increasing the GO content promotes more complete core–shell formation, leading to higher zeta potentials, stronger interfacial polarization, and higher electrical performance. After embedding in PAA, the LMP@rGO structures form hydrogen-bonding networks with the hydrogel nature, improving both dielectric constant and charge retention while notably preserving soft mechanical compliance. The resulting LMP@rGO/PAA hydrogels show enhanced triboelectric output, with the 2.0 wt/vol% composite generating sufficient power to illuminate more than half of 504 series-connected LEDs. All the results demonstrate the potential of LMP@rGO hydrogel composites as promising triboelectric layer materials for next-generation wearable and self-powered electronic systems. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

Aiming at the chattering issue in speed observation for sensorless control, this paper proposes a sliding mode observer based on an improved double-power reaching law for high-performance speed estimation in five-phase induction motors. Traditional constant-rate reaching law observers exhibit significant chattering, while the double-power reaching law, though offering certain “variable-gain” adjustment effects, still has limited chattering suppression capability. To address this, the paper introduces a state variable related to the stator current into the conventional double-power observer, further enhancing the ability of the sliding mode gain to vary with the system state. This approach effectively suppresses chattering while maintaining convergence speed. The stability of the observer system employing the new reaching law is proven using Lyapunov stability theory, and the value ranges of key parameters are determined. Simulation results demonstrate that, compared to traditional constant-rate reaching law and conventional double-power reaching law observers, the proposed improved method significantly reduces speed observation chattering and effectively enhances the observation accuracy of the observer. Full article

High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Full article

This paper addresses Region-3 control of a 2.5 MW three-bladed HAWT using a data-driven workflow that links empirical modeling to implementable pitch control. To focus on fundamental regulation dynamics, the turbine is modeled as a rigid single-mass drivetrain driven by identified quasi-steady aerodynamics. First, we identify a compact shaft-power surface $P(\omega ,V,\beta )$ and recover the associated MPP condition, which clarifies why the optimal rotor speed rises with wind and motivates a comparison between capped-MPP operation and constant-speed regulation. We then synthesize a practical Region-3 loop—PI in rate with a first-order pitch servo and saturation handling—and evaluate proportional (P), PI, and PI + servo controllers under sinusoidal and Kaimal-turbulent inflow. Finally, we propose an adaptive PI variant that keeps a fixed acceleration feed-through but retunes the integral path online via ARX(1,1) + RLS to maintain a target closed-loop bandwidth. Performance metrics computed over the full simulation window (t ∈ [0, 50] s) show that P-only control exhibits large steady bias and cap violations; PI recenters speed and power around their targets; adding a pitch servo further trims peaks and ripple. In steady-state turbulent tests, PI + servo achieves tight regulation, Δω_peak ≈ 0.033% (0.079 rad/s), P_RMS ≈ 0.62%, while the adaptive PI maintains similar tightness with the lowest variability overall Δω_peak ≈ 0.0104% (0.025 rad/s), P_RMS ≈ 0.17. The workflow yields a practically implementable β(V) schedule and a lightweight adaptation mechanism that compensates for slow aerodynamic performance drift without changing the control structure. While structural loads and aeroelastic modes are not explicitly modeled, the proposed controller enforces strict speed and power constraints via a rigid-body dynamic analysis. Extensions to IPC, preview/forecast augmentation, and validation on higher-fidelity aeroelastic/drivetrain models are identified as future work. Full article

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control. Full article

The modular multi-active bridge (MMAB) converter—characterized by electrical isolation, modular design, high power density, and high efficiency—can be readily scaled to multiple DC ports through an internal shared high-frequency bus (HFB), establishing it as a viable topology for DC transformer (DCT) applications. However, its interconnection to a DC grid via low-damping inductors may provoke low-frequency oscillations and instability. To mitigate this issue, this paper employs a pole-zero cancellation approach to model the conventional constant-power control (CPC) loop as a second-order system, thereby elucidating the relationship between equivalent line impedance and stability. An active damping control strategy based on virtual impedance is then introduced, supported by systematic design guidelines for the damping compensation stage. Simulation and experimental results confirm that under weak damping conditions, the proposed method raises the damping coefficient to 0.707 and effectively suppresses low-frequency oscillations—all without altering physical line impedance, introducing additional power losses or requiring extra sensing devices—thereby markedly improving grid-connected stability. Full article

To enhance grid stability with high renewable energy penetration, this paper proposes an adaptive virtual synchronous generator (VSG) control using a backpropagation neural network (BPNN). Traditional VSG control methods exhibit limitations in handling nonlinear dynamics and suppressing power oscillations. Distinguishing from existing studies that apply BPNN solely for damping adjustment, this paper proposes a novel strategy where BPNN simultaneously regulates both VSG virtual inertia and damping coefficients by learning nonlinear relationships among inertia, angular velocity deviation, and its rate of change. A key innovation is redesigning the error function to minimize angular acceleration changes rather than frequency deviations, aligning with rotational inertia’s physical role and preventing excessive adjustments. Additionally, an adaptive damping coefficient is introduced based on optimal damping ratio principles to further suppress power oscillations. Simulation under load disturbances and grid frequency perturbations demonstrates that the proposed BPNN strategy significantly outperforms constant inertia, bang–bang, and radial basis function neural network methods. Full article

The magnonic activity refers to a chiral effect in the field of magnetization dynamics that exhibits a high degree of analogy to optical activity. It manifests as the azimuthal continuous rotation of standing-wave nodes in the cross-section of spin waves during propagation in ferromagnetic nanowire waveguides. The study employs micromagnetic simulation methods to theoretically investigate the influence of the interfacial Dzyaloshinskii–Moriya interaction (iDMI) on the magnonic activity in longitudinally magnetized ferromagnetic nanotubes. The results demonstrate that iDMI-induced chirality effectively controls the magnonic activity’s rotatory power, which relies on the values of the iDMI constant D (from $-0.5 \mathrm{m}\mathrm{J}/{\mathrm{m}}^2$ to $1 \mathrm{m}\mathrm{J}/{\mathrm{m}}^2$). Additionally, nanotube thickness variations (from 3 nm to 15 nm) alter effective curvature, further influencing the rotatory power of the magnonic activity. Numerical simulations and semi-analytical calculations show excellent agreement, providing a theoretical foundation for chiral spin-wave manipulation in 3D curved nanostructures. Full article

This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system’s initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system’s adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance. Full article

Hybrid trolleybuses (HTBs) with in-motion charging (IMC) can extend zero-emission service using existing catenary, but high on-wire charging powers may concentrate loads and accelerate battery aging. We present a data-driven simulation that replays recorded high-resolution Controller Area Network (CAN) logs through a per-vehicle electrical model with (Constant-Current/Constant-Voltage) (CC/CV) charging and a stress-map aging estimator, a configurable partial catenary overlay, and fleet aggregation by simple summation and an iterative node-voltage analysis of a resistor-network catenary model. A parameter sweep across battery sizes, upper state of charge (SoC) bounds, and charging power caps compares a minimal “charge-whenever-possible” policy with a per-vehicle lookahead (“oracle”) policy that spreads charging over available catenary time. Results show that lowering maximum charging power and/or the upper SoC bound reduces capacity fade, while energy-demand differences are small. Fleet load profiles are dominated by timetable-driven concurrency using 40 recorded days overlaid into one synthetic day: varying per-vehicle power or target SoC has little effect on peak demand; per-vehicle lookahead does not flatten the peak. The node-voltage analysis indicates catenary efficiency around 97% and fewer undervoltage events at lower charging powers. We conclude that per-vehicle policies can reduce battery stress, whereas peak shaving requires cooperative, fleet-level scheduling. Full article

Energy efficiency is a key requirement for Internet of Things (IoT) nodes, particularly in applications powered by energy harvesting that operate without batteries. In this work, we present a parametric power model of a LoRa transceiver (Semtech SX1276) aimed at ultra-low power remote sensing scenarios. The transceiver was characterized in all relevant states (startup, transmission, reception, and sleep), and the results were used to build a state-based model that predicts average power consumption as a function of transmission power, sleep strategy, packetization, and input data rate. Experimental validation confirmed that the cubic fit for transmission peaks achieves a determination coefficient of 0.99, while reception is added as a constant consumption. The model was implemented in a Python simulator that provides mean, best-case, and worst-case estimates of system power consumption, and it was validated in an ASIC-based sensor node demonstration, with predictions within 10% of measured values. The framework highlights the trade-offs between energy efficiency and robustness (e.g., minimal SF and no CRC vs. higher spreading factors and error-control) and supports the design of custom controllers for ultra-low power IoT nodes as well as more energy-permissive applications. Full article

For the study, we used four kerosene-based ferrofluid samples containing magnetite nanoparticles stabilized with oleic acid. Starting from the initial sample (A0), the other three samples were obtained by dilution with kerosene. The complex magnetic permeability measurements were performed in the microwave region (0.5–6) GHz, for different H values of the polarizing magnetic field, between (0–115) kA/m. These measurements revealed the ferromagnetic resonance phenomenon for each sample, allowing the determination of the anisotropy field (H_A) and the effective anisotropy constant (K_eff) of nanoparticles, depending on the volume fraction of particles (φ). At the same time, the measurements allowed the determination of the specific magnetic loss power (p_m), effective heating rate (HR_eff), intrinsic loss power (ILP), and specific absorption rate (SAR) as functions of the frequency (f) and magnetic field (H), of all investigated samples, using newly proposed equations for their calculation. For the first time, this study evaluates the maximum limit of the applied polarizing magnetic field (H_max ≈ 80 kA/m) and the minimum limit volume fraction of nanoparticles (φ_min ≈ 3.5%) at which microwave heating of the ferrofluid remains efficient. At the same time, the results obtained show that the temperature increase of the ferrofluid samples, upon interaction with a microwave field, can be controlled by varying both H and φ, pointing to possible applications in magnetic hyperthermia. Full article