Search Results (4,031)

Radio frequency (RF) energy harvesting offers a potential power source for low-power Internet of Things and wireless sensing nodes, but compact rectifiers must balance impedance matching, multiband response, and load-driving capability. This work presents a compact SMS7621 Schottky-diode RF rectifier for RF-powered wireless sensing applications. An 11-segment microstrip distributed-parameter collaborative optimization strategy is used to tune impedance transformation in a 3.48 cm × 1.98 cm single-layer layout while compensating for diode nonlinear impedance variation and package parasitics. Simulations show more than 40% RF-to-DC conversion efficiency from 1.90 to 2.35 GHz, with additional efficiency peaks of 40.55% at 4.45 GHz and 38.45% at 7.15 GHz. Measurements verify the 2.45 GHz output performance under controlled high-input-power excitation: with a 300 Ω load and 25 dBm input, the rectifier delivers a maximum DC voltage of 5.42 V. At 15 dBm input, the measured peak efficiency reaches 46.05% at 2 GHz and remains 35.69% at 4 GHz. These results indicate a compact rectifier front end with multiband harvesting potential and 5 V-class load-driving capability under dedicated RF powering conditions. Full article

Active magnetic bearings (AMBs) require accurate dynamic models for controller design and performance analysis, but their inherent open-loop instability makes modeling difficult under practical operating conditions. This study presents a closed-loop black-box identification method for an AMB system under decentralized control. A pseudo-random binary sequence (PRBS) excitation was injected into the closed-loop system, and the measured input–output data were used to estimate a nonparametric frequency-response model. The effects of excitation amplitude were first examined, and an excitation level of about 10–12% of the saturation current was found to provide a suitable balance among coherence, signal-to-noise ratio, and frequency-response variance. Based on the obtained frequency-domain data, ARX, output-error (OE), and state-space (SS) models were identified and compared. An initial model order range was estimated using the ARX structure and quantitative criteria, including the loss function and Bayesian information criterion. Within this candidate range, different model structures and orders were further evaluated. The 7th-order SS model showed the best overall agreement with the nonparametric frequency response and captured the dominant dynamic features more accurately. Independent time-domain validation and closed-loop reconstruction further confirmed that the selected SS model can represent the practical AMB dynamics with acceptable accuracy. Full article

Reliable condition monitoring of wind turbine gearboxes is critical to reducing unplanned downtime and maintenance costs in wind farms. However, this task presents significant challenges due to the non-stationary nature of vibration signals, in which fault-relevant features are sparsely and unevenly distributed across the time–frequency map. Although time–frequency analysis has been widely adopted to represent nonlinear and non-stationary vibration signals, existing deep learning methods typically process the full spectrogram directly, without distinguishing redundant or uninformative regions. This leads to high input dimensionality and exposes the model to substantial spectral noise. Consequently, it increases computational burden and potentially reduces the diagnostic reliability. To address this issue, this paper proposes a two-stage hybrid framework based on complementary selection mechanisms operating on two distinct feature spaces. In the first stage, eXtreme Gradient Boosting (XGBoost) importance scores are used to identify and permanently prune uninformative time–frequency features from the input spectrogram, reducing the input map size by 25%. In the second stage, a Squeeze-and-Excitation (SE) block, inserted after the deepest residual layer, performs soft channel-wise recalibration of the abstract feature maps produced by the residual convolutional neural network (ResCNN), thereby amplifying discriminative representations prior to classification. The proposed method was evaluated in an eight-class variable-speed fault classification task using the MCC5-THU benchmark, where data were collected from a 2.2 kW motor-driven gearbox test rig. The proposed method achieves a mean accuracy of 97.81% ± 0.33% under 5-fold stratified cross-validation (CV), while reducing classifier training time by approximately 23% compared to a baseline model trained on the full spectrogram. These results demonstrate that explicit input-level spectrogram pruning, combined with model-level channel attention, yields a robust and computationally efficient diagnostic framework for wind turbine gearbox condition monitoring. Full article

Petrophysical parameters of rocks and ores, influenced by composition, porosity, temperature, and pressure, are generally distributed uniformly or continuously in space—relatively homogeneous within individual geological units and varying smoothly across stratigraphic transition zones and contact boundaries. Based on this geological characteristic, this paper establishes a three-dimensional (3-D) piecewise linear continuous spectral parameter model to compute forward responses of apparent spectral parameters under low-frequency current excitation. The calculation follows a two-step workflow: finite-element forward simulation of multi-frequency apparent complex resistivity, followed by recursive inversion to obtain apparent spectral parameters. The subsurface medium is discretized with hexahedral meshes, with four Cole–Cole parameters (zero-frequency resistivity, chargeability, time constant, and frequency exponent) assigned to each mesh node. Linear interpolation is adopted for complex resistivity and potential within each element, ensuring piecewise linear continuity of both physical properties and simulated fields. To improve accuracy, the total complex potential is decomposed into a primary field from the source current and a secondary field from complex conductivity variations, and the corresponding boundary value problem and variational form are derived. On this basis, we implement the finite-element algorithm for 3-D piecewise linear continuous media and the recursive inversion algorithm for spectral parameters, and develop an interactive 3-D SIP forward modeling program. Comparison with analytical solutions for a continuous layered model shows good agreement, with relative errors below 1.5% for the real part and 3.8% for the imaginary part of apparent complex resistivity. Two numerical cases—a cubic anomaly in homogeneous half-space and a sandbox model—further verify the performance of the proposed method. Full article

Conventional I/O Buffer Information Specification (IBIS) models often suffer from reduced fidelity in high-speed signaling because their static table-lookup mechanism cannot accurately reproduce complex transient I/O-buffer dynamics. To address this limitation, this study proposes a Verilog-AMS-based dynamic parameter compensation method. First, the conventional IBIS model is reformulated into a three-layer architecture comprising a data interface layer, an intermediate variable computation layer, and a port response synthesis layer. Then, based on Kirchhoff’s current law (KCL), the monotonic dependence of the output voltage on the pull-up and pull-down driving factors, k_pu and k_pd, is analytically derived to provide a directional criterion for parameter correction. Building on this criterion, a pulse-width-driven compensation algorithm is developed by constructing a pulse-width-indexed dual-factor empirical adjustment matrix and detecting the pulse width of the input bitstream in real time during transient simulation. The detected pulse width is then used to dynamically update k_pu and k_pd, enabling the IBIS response to converge toward the transistor-level SPICE reference waveform. Three representative device models were evaluated at 666 Mbps and 1.302 Gbps using pseudo-random binary sequence excitation, and the model fidelity was quantified using the normalized mean square error (NMSE). The proposed method reduced the NMSE from −6.73 to −1.03 dB before compensation to −54.79 to −44.19 dB after compensation, demonstrating a substantial improvement in high-frequency IBIS modeling fidelity and confirming the robustness and adaptability of the pulse-width-aware dynamic compensation strategy under random high-speed excitation. Full article

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

Bolted joints are critical load-transfer components in bridges, wind turbines, aerospace systems, mechanical equipment, and offshore platforms, where preload loss can degrade stiffness, accelerate fatigue, and compromise safety. For structural health monitoring, early monitoring of preload reduction before marked loosening is essential, yet existing ultrasonic guided wave indicators remain affected by frequency dependence, non-monotonic responses, amplitude drift, and environmental disturbances. This study proposes an early-warning-oriented preload identification method that combines broadband excitation, multi-frequency narrowband reconstruction, spectral centroid extraction, optimized weighted fusion, and fixed SC-domain linear calibration from one reference loading group. Using a 20–250 kHz Chirp response, 14 narrowband signals from 50 to 180 kHz were reconstructed for an M20 single-bolt specimen tested over 50–90 N·m. The fused spectral centroid index exhibited a stable, monotonic, and approximately linear relationship with preload. When fixed weights and calibration coefficients were transferred to held-out repeated-loading groups, all Pearson correlation coefficients exceeded 0.99. Feature-level robustness tests showed that the arithmetic mean of the spectral centroid reduced temperature-induced Range% by 98.42–99.08% and RSD by 98.89–99.31% relative to energy-based features. This work provides an interpretable multi-frequency spectral descriptor and a calibration transfer framework for repeatable early warning of preload loss in a controlled single-bolt configuration. Full article

We carried out a study on adjusting the voltage at the output terminals of a three-phase synchronous generator using the voltage encoder EMBSIN 121u. The purpose of this study was to increase the quantity and quality of the electrical energy produced by the generator. This paper is innovative as the author generates three models in MATLAB-Simulink to study voltage adjustment in a three-phase synchronous generator with electromagnetic excitation in two distinct cases: case 1, running the three-phase synchronous generator with a variable load and constant frequency, and case 2, running this generator with a constant load and variable frequency. In the first case, the voltage is adjusted through an automatic voltage adjustment system equipped with a proportional integrative (PI) controller (model 1) or through a fuzzy logic (FL) controller (model 2). The voltage is adjusted in the second case through an automatic voltage adjustment system equipped with a PI controller (model 3). In the case of the automatic voltage adjustment system with a fuzzy logic controller, the electrical energy supplied by the three-phase synchronous generator will be higher than in the case of the automatic voltage adjustment system equipped with a PI controller (at the moment, t = 6 s: $S_{gen\_PI}=158.2$ (VA) and $S_{gen\_FL}=230.7$ (VA)). Moreover, to implement the adjustment algorithm of the three-phase synchronous generator voltage through the voltage encoder EMBSIN 121u, the author has created a program in the programming environment Arduino IDE. The results of this study could also be used for three-phase synchronous generators with electromagnetic excitation used to construct wind power stations. Full article

Background/Objectives: Gamma-aminobutyric acid (GABA) is increasingly recognized as an important modulator of pancreatic beta-cell function, but the mechanisms by which it regulates intracellular Ca²⁺ oscillations and coordinated beta-cell activity remain insufficiently understood. The aim of this study was to investigate how GABA influences the amplitude, frequency, phase adjustment, entrainment, and synchronization of beta-cell Ca²⁺ oscillations. Methods: We developed a reduced ATP–Ca²⁺ oscillation model, based on established beta-cell oscillatory frameworks, and coupled it to the GABA-shunt subsystem derived from our previously established Dual Anaplerotic Model. The model incorporates explicit dynamics of cytosolic Ca²⁺, endoplasmic reticulum Ca²⁺, ATP, and a regulatory variable controlling Ca²⁺ influx, while the interstitial GABA signal is represented as a delayed feedback signal acting on cellular excitability. Single-cell and two-cell simulations were performed to analyze GABA-dependent oscillatory regulation and intercellular coupling. Results: The model reproduced key experimental observations under both control and GABA-deficient conditions, including reduced Ca²⁺-oscillation amplitude and a prolonged oscillation period when GABA production was suppressed. Mechanistically, GABA affected single-cell oscillations through two complementary pathways: metabolically, by modulating ATP production through PEP-related and TCA-related contributions linked to the GABA shunt, and as an interstitial/paracrine signal, by adjusting the phase of Ca²⁺ influx through fast and delayed inhibitory feedback. In the reduced two-cell model, delayed interstitial GABA signaling could phase-lock non-identical oscillators over finite ranges of parameter mismatch. When included as an additional weak effective term, electrical coupling broadened these ranges, consistent with a complementary interaction between GABA-mediated phase adjustment and established electrical coupling. Conclusions: GABA acts as a dual regulator of beta-cell dynamics, linking intracellular metabolism to Ca²⁺-oscillation patterning and promoting coordinated activity through intercellular phase adjustment. The model provides a mechanistic framework connecting GABA metabolism, ATP dynamics, Ca²⁺ signaling, and beta-cell synchronization in pancreatic islets. Full article

Extreme and sudden temperature fluctuations observed as a result of global climate change increase the environmental pressure on energy transmission infrastructure. These meteorological changes significantly increase the risk of failure for porcelain insulators, which exhibit low thermal resistance and are susceptible to sudden arcing and surface deformations. In this study, a hybrid CNN–BiLSTM–SE architecture augmented with the Squeeze-and-Excitation attention mechanism is proposed using surface leakage current signals to diagnose healthy, cracked, and broken structural conditions in three-unit porcelain insulators. The SE block in the architecture dynamically rescales feature maps from CNN layers on a channel-by-channel basis. Thus, it highlights the signal characteristic that is dominant for fault diagnosis just before the BiLSTM units learn temporal dependencies. Leakage current data were obtained under an experimental setup at 60 kV for 15 different conditions covering all possible combinations of healthy, cracked, and broken insulator units. The raw signals were preprocessed with the Savitzky–Golay filter to suppress noise while preserving the diagnostic waveform morphology. 24 features covering time-domain statistics, frequency-domain spectral characteristics, and wavelet-domain energy components were extracted and used as model inputs. The CNN–BiLSTM–SE architecture achieved a classification accuracy of 93.83%, surpassing the standalone CNN (88.89%), BiLSTM (87.65%), and CNN–BiLSTM (91.36%) models, as well as classical machine-learning baselines (SVM: 87.65%, Random Forest: 90.12%, Boosted Trees: 87.65%). Full article

In this work, the structural, electronic, optical, and vibrational properties of gallium nitride (Ga_xN_x) nanostructures were systematically investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT). A series of nanoparticles was constructed starting from a cubic-like Ga₄N₄ building unit, leading to one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and hexagonal configurations. Geometry optimizations and vibrational frequency calculations were performed at the B3LYP/def2-TZVP level, while optical properties were investigated using TD-DFT with the CAM-B3LYP functional. Only dynamically stable structures without imaginary vibrational frequencies were considered for spectroscopic analysis. The results reveal a strong dependence of the optical and vibrational behavior on nanoparticle size and geometry. Larger and lower-symmetry systems exhibit broader and red-shifted UV–Vis absorption spectra together with richer IR vibrational features. In contrast, elongated low-dimensional configurations such as Ga₁₂N₁₂–1D and Ga₁₆N₁₆–1D/2D were found to be dynamically unstable. The investigated nanostructures also show a clear tendency toward structural reorganization from cubic-like motifs to compact hexagonal arrangements related to the wurtzite phase of bulk GaN. Benchmark analysis demonstrates that CAM-B3LYP provides reliable excitation energies at moderate computational cost. Overall, the obtained results highlight the strong coupling between structure and optoelectronic properties in Ga_xN_x nanostructures and indicate their potential for nanoscale optoelectronic and photonic applications. Full article

Side-by-side mooring alongside a quay is an effective strategy to enhance berth utilization and port efficiency. However, in confined waters, hydrodynamic interactions among adjacent ships and the quay wall can significantly alter ship motions, wave loads, mooring tensions, and fender reactions, thereby affecting operational safety. In this study, frequency- and time-domain numerical models of side-by-side moored ships at a quay were developed using the ANSYS AQWA 2025 R1 hydrodynamic analysis software (hereafter AQWA). The S175 and Wigley III hulls were selected as representative vessels. After model validation and mesh independence analysis, the hydrodynamic response of the inboard ship was examined under varying wave headings, water depths, quay boundary conditions, and outboard ship scales. Mooring loads were further analyzed under regular waves, irregular waves, and combined wave–current conditions. The results indicate that side-by-side mooring significantly modifies the sway, heave, and roll responses of the inboard ship. While the outboard ship generally provides a shielding effect, local response amplification may still occur within specific frequency ranges. The wave heading, shallow water, and quay wall effects play dominant roles in redistributing wave excitation forces, added mass, and radiation damping. Time-domain simulations reveal that ship-to-ship mooring lines S2 and S3 are the critical load-bearing components, especially under beam waves and certain oblique current directions. The use of polyester mooring lines effectively reduces peak tensions in critical lines but may increase fender reactions. These findings provide a reference for hydrodynamic safety assessment and mooring optimization in side-by-side berthing operations. Full article

In traditional mechanical de-icing technologies, the time-frequency evolution and spatial propagation mechanisms of transient high-frequency impact signals in flexible transmission lines remain unclear. To address this issue, transient impact responses were experimentally investigated using a full-scale transmission line model. An active de-icing exciter, featuring controllable impact energy and the potential for sustained online operation, was independently developed. High-frequency transient acceleration signals were acquired at multiple measurement points on a 20 m single-span line. The spatial distribution and time-frequency attenuation characteristics of the impact energy were quantitatively evaluated by extracting high-order time-domain statistical features, including root mean square, kurtosis, and crest factor, together with frequency-domain analyses based on Fast Fourier Transform (FFT) and wavelet entropy. The results indicate that: (1) The exciter generated highly impulsive transient responses, with a kurtosis up to 795.3 and a crest factor approaching 40. This suggests a strong local concentration of impact energy at the excitation source, which provides a dynamic basis for analyzing potential localized stress concentration and dynamic responses of the conductor system. (2) The transmission line structure exhibited a significant low-pass filtering effect on transient high-frequency shock waves. As the shock wave propagated towards the distal end, its high-frequency components above 30 Hz were substantially attenuated, likely due to internal dry friction within the stranded conductor. Consequently, the dominant frequency decreased to a low-frequency macroscopic sway of approximately 12 Hz, indicating a reduced risk of transmitting high-frequency shock loads to distal fittings and towers. (3) Under geometric nonlinear coupling, the vertical impact energy was partially transferred to the longitudinal and lateral directions during propagation, leading to sustained out-of-plane swaying. This study reveals the signal evolution characteristics of transient impacts in overhead transmission lines and provides experimental evidence for optimizing excitation parameters and assessing the engineering safety of active impact de-icing technologies. Full article

Active seat suspensions are a potential approach for reducing vertical vibration exposure in vehicle and construction-machinery seats. In most existing studies on active seat-suspension control, acceleration signals are rarely used as direct feedback because of their high noise sensitivity. However, acceleration can be measured at low cost and directly reflects ride comfort, which makes it attractive for prototype-level vibration control. This paper proposes an acceleration-feedback-based adaptive weighted control strategy for a motor-driven active seat-suspension prototype with input delay. A 2-DOF driver-seat model is employed to describe the dominant vertical dynamics. An auxiliary virtual state variable is introduced to embed a deformation-dependent weighting mechanism into the control objective, allowing the controller to coordinate ride-comfort improvement and suspension-stroke safety according to real-time suspension deformation. Based on the Linear Matrix Inequality (LMI) method, a state-feedback H-infinity controller is synthesized while considering actuation delay and input saturation. The stability of the controlled system is proved under the stated model assumptions, and the controller performance is examined through numerical simulation and laboratory prototype experiments. The acceleration transmissibility from the vibration-platform floor to the driver is evaluated experimentally in the frequency domain, and random-excitation responses are investigated through both simulation and experimentation. The results show that the proposed strategy can reduce the dominant vibration responses and satisfy the imposed stroke and actuation constraints on the laboratory test rig. Full article