Search Results (4,176)

Rogowski coils are increasingly being used in electricity metering systems. However, owing to their operating principle, they require an additional active integrating circuit to produce an output voltage or current that is directly proportional to the input current. A signal conditioner has the most significant impact on the overall conversion accuracy of the combined transducer. In this paper, a new measurement system and testing procedure utilizing a digital power meter and arbitrary waveform generator are proposed. This approach enables the characterization of the conversion accuracy of both types of active integrators: voltage-to-voltage and voltage-to-current converters. The conversion error for distorted input voltage harmonics and additional phase shift across a range of frequencies are determined. Instead of using the actual signal from the Rogowski coil during testing —which would be challenging owing to the required high RMS value of the distorted current for its input and difficulties in accurately measuring the RMS values of harmonics and their phase angles in relation to the output voltage or current of the tested converter—an arbitrary waveform generator is used. The input voltage to the active integrating circuit replicates the output voltage of the Rogowski coil: as the harmonic order increases, its RMS voltage rises proportionally. Full article

With the advancement of electromagnetic interference and counter-interference technology, complex and unpredictable interference signals greatly reduce radar detection, tracking, and recognition performance. In multi-interference environments, the overlap of interference cross-correlation peaks can mask target signals, weakening radar interference suppression capability. To address this, we propose a joint waveform and filter design method called Multi-Interference Suppression Network (MISNet) for effective interference suppression. First, we develop a design criterion based on suppression coefficients for different interferences, minimizing both cross-correlation energy and interference peak models. Then, for the non-smooth, non-convex optimization problem, we use complex neural networks and gating mechanisms, transforming it into a differentiable problem via end-to-end training to optimize the transmit waveform and receive filter efficiently. Simulation results show that compared to traditional algorithms, MISNet effectively reduces interference cross-correlation peaks and autocorrelation sidelobes in single interference environments; it demonstrates excellent robustness in multi-interference environments, significantly outperforming CNN, PSO, and ANN comparison methods, effectively improving radar interference suppression performance in complex multi-interference scenarios. Full article

Chaos-based wireless communication systems can enhance the physical-layer security of IoT devices, but their reliability depends on stable chaotic behavior under real conditions. We investigate a modified Colpitts oscillator with a tunable base bias voltage, introduced as an independent control parameter to flexibly adjust nonlinear regimes. Using numerical studies, SPICE simulations, and hardware experiments, we show that simplified numerical models predict only a DC offset shift, whereas realistic implementations reveal qualitative changes in the dynamics, highlighting the need for experimental validation. We further demonstrate hybrid synchronization between the analog oscillator and an FPGA-based digital model. Despite model simplifications and non-idealities, synchronization is successfully achieved using the Pecora–Carroll method, showing that preserving the core dynamic structure is more critical than exact waveform replication. These results clarify the constraints of idealized models for predicting dynamical patterns while confirming the robustness of hybrid synchronization for secure, resource-constrained communication systems. Full article

Despite the wide applications of full-waveform light detection and ranging (FW-LiDAR) on target detection and recognizing, topographical mapping, and ecological management, etc., the mapping between the echo waveform and the properties of the targets, even for typical three-dimensional (3D) targets, has not been established. The mechanics of the modulation of targets on the echo waveform is thus ambiguous, constraining the retrieval of target properties in FW-LiDAR. This paper derived the formula of echo waveform modulated by typical 3D targets, namely, a rectangular prism, a regular hexagonal prism, and a cone. The modulation of shape, size, position, and attitude of 3D targets on the echo waveform has been investigated extensively. The results showed that, for prisms, variations in the echo waveforms under various factors essentially arise from changes in the inclination angles of their reflective surfaces and their positions relative to the laser spot. For cones, their echo waveforms can be approximated and analyzed using isosceles triangular micro-facets. The work in this paper is helpful in probing the modulation of 3D targets on echo waveform, as well as extracting the properties of 3D targets in FW-LiDAR domains, which are significant in areas ranging from topographical mapping to space debris monitoring. Full article

This paper analyses a special case in evaluating the passive autoranging (AR) technique that dynamically extends the measurement range of a fiber Bragg grating/piezoelectric transducer (FBG/PZT) operating with a current transformer (CT) to realize a dual-purpose metering and protection photonic current transducer (PCT). The technique relies on shorting serially connected burden resistors operating with the CT, using MOSFET switches that react to a changing input current to extend measurement range. The rapid changes in the voltage at the FBG/PZT transducer that are associated with the MOSFET switching are then used on the FBG interrogator side to select the correct measurement range. However, when the MOSFET switching in the AR circuit occurs near the zero-crossing of the input current, the rapid changes in the voltage presented to the FBG/PZT no longer occur, rendering the correct range setting at the interrogator side problematic. The basic switching detection algorithm based on voltage derivative (dV/dt) thresholds proposed in the previous research is not sufficiently sensitive in these conditions, leading to incorrect range selection. To address this, a new detection algorithm based on temporal slope differencing around the zero-crossing is proposed as an additional detection mechanism for these special cases. Thus, the improved hybrid algorithm additionally computes the derivative dV/dt at the FBG/PZT voltage signal within a focused 6 ms temporal window centered around the zero-crossing point, a 3 ms window before and after each zero-crossing instance. It then compares the difference between these two values to a predefined threshold. If the difference exceeds the threshold, a switching event is identified. This method reliably detects even subtle switching events near zero crossings, enabling the accurate reconstruction of the burden current. The performance of the improved algorithm is validated through simulations and experimental results involving zero-crossing switching scenarios. Results indicate that the proposed algorithm improves MOSFET switching detection and facilitates reliable waveform reconstruction without requiring additional hardware. Full article

Accurate estimation of the forest canopy height is crucial in monitoring the global carbon cycle and evaluating progress toward carbon neutrality goals. The Global Ecosystem Dynamics Investigation (GEDI) mission provides an important data source for canopy height estimation at a global scale. However, the non-zero half-width of the transmitted laser pulses (NHWTLP) and the influence of terrain slope can cause waveform broadening and overlap between canopy returns and ground returns in GEDI waveforms, thereby reducing the estimation accuracy. To address these limitations, we propose a canopy height retrieval method that combines the deconvolution of GEDI’s transmitted waveforms with terrain slope constraints on the ground response function. The method consists of two main components. The first is performing deconvolution on GEDI’s effective return waveforms using their corresponding transmitted waveforms to obtain the true ground response function within each GEDI footprint, thereby mitigating waveform broadening and overlap induced by NHWTLP. This process includes constructing a convolution convergence function for GEDI waveforms, denoising GEDI waveform data, transforming one-dimensional ground response functions into two dimensions, and applying amplitude difference regularization between the convolved and observed waveforms. The second is incorporating terrain slope parameters derived from a digital terrain model (DTM) as constraints in the canopy height estimation model to alleviate waveform broadening and overlap in ground response functions caused by topographic effects. The proposed approach enhances the precision of forest canopy height estimation from GEDI data, particularly in areas with complex terrain. The results demonstrate that, under various conditions—including GEDI full-power beams and coverage beams, different terrain slopes, varying canopy closures, and multiple study areas—the retrieved height (rh) model constructed from ground response functions derived via the inverse deconvolution of the transmitted waveforms (IDTW) outperforms the RH (the official height from GEDI L2A) model constructed using RH parameters from GEDI L2A data files in forest canopy height estimation. Specifically, without incorporating terrain slope, the rh model for canopy height estimation using full-power beams achieved a coefficient of determination (R²) of 0.58 and a root mean square error (RMSE) of 5.23 m, compared to the RH model, which had an R² of 0.58 and an RMSE of 5.54 m. After incorporating terrain slope, the rh_g model for full-power beams in canopy height estimation yielded an R² of 0.61 and an RMSE of 5.21 m, while the RH_g model attained an R² of 0.60 and an RMSE of 5.45 m. These findings indicate that the proposed method effectively mitigates waveform broadening and overlap in GEDI waveforms, thereby enhancing the precision of forest canopy height estimation, particularly in areas with complex terrain. This approach provides robust technical support for global-scale forest resource assessment and contributes to the accurate monitoring of carbon dynamics. Full article

Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection. Full article

Artificial intelligence (AI) has increasingly become a transformative tool in cardiology, particularly in diagnosing and managing atrial fibrillation (AF), the most prevalent cardiac arrhythmia. This review aims to critically assess and synthesize current AI methodologies and their clinical relevance in AF diagnosis, risk prediction, and therapeutic guidance. It systematically evaluates recent advancements in AI methodologies, including machine learning, deep learning, and natural language processing, for AF detection, risk stratification, and therapeutic decision-making. AI-driven tools have demonstrated superior accuracy and efficiency in interpreting electrocardiograms (ECGs), continuous monitoring via wearable devices, and predicting AF onset and progression compared to traditional clinical approaches. Deep learning algorithms, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have revolutionized ECG analysis, identifying subtle waveform features predictive of AF development. Additionally, AI models significantly enhance clinical decision-making by personalizing anticoagulation therapy, optimizing rhythm versus rate-control strategies, and predicting procedural outcomes for catheter ablation. Despite considerable potential, practical adoption of AI in clinical practice is constrained by challenges including data privacy, explainability, and integration into clinical workflows. Addressing these challenges through robust validation studies, transparent algorithm development, and interdisciplinary collaborations will be crucial. In conclusion, AI represents a paradigm shift in AF management, promising improvements in diagnostic precision, personalized care, and patient outcomes. This review highlights the growing clinical importance of AI in AF care and provides a consolidated perspective on current applications, limitations, and future directions. Full article

The prediction of thin-bedded, favorable sand bodies within the Triassic Baikouquan Formation fan delta on the western slope of the Mahu Sag is challenging due to their strong spatial heterogeneity. To address this, we propose an integrated workflow that synergizes seismic sedimentology with geologically constrained seismic inversion. This study leverages well logging, core data, and 3D seismic surveys. Initially, seismic attribute analysis and stratal slicing were employed to delineate sedimentary microfacies, revealing that the fan delta front subfacies comprises subaqueous distributary channels, interdistributary bays, and distal bars. Subsequently, the planform distribution of these microfacies served as a critical constraint for the Seismic Waveform Indicative Inversion (SWII), effectively enhancing the resolution for thin sand body identification. The results demonstrate the following: (1). Two NW-SE trending subaqueous distributary channel systems, converging near the BAI65 well, form the primary reservoirs. (2). The SWII, optimized by our workflow, successfully predicts high-quality sand bodies with a cumulative area of 159.2 km², primarily located in the MAXI1, AIHU10, and AICAN1 well areas, as well as west of the MA18 well. This study highlights the value of integrating sedimentary facies boundaries as a geological constraint in seismic inversion, providing a more reliable method for predicting heterogeneous thin sand bodies and delineating future exploration targets in the Mahu Sag. Full article

This study addresses the challenge of cognitive waveform design for multiple-input–multiple-output (MIMO) radar systems operating in cluttered environments. It focuses on the key practical requirements for transmitting time-domain waveforms and proposes a novel approach. This method first determines the optimal frequency-domain waveform and then designs a time-domain waveform that closely approximates the frequency-domain solution. The primary objective is to enable MIMO radar systems to transmit orthogonal waveforms while accommodating various constraints. A frequency-domain waveform optimization model was initially developed using the principle of maximizing dual mutual information (DMI), and the energy spectral density (ESD) of the optimal waveform was derived using the water-filling method. Next, a time-domain waveform approximation model is constructed based on the minimum mean square error (MMSE) criterion, which incorporates constant modulus and peak-to-average power ratio (PAPR) constraints. To minimize the performance degradation of the waveform, an improved adaptive gradient descent genetic algorithm (GD-AGA) was proposed to synthesize multichannel orthogonal time-domain waveforms for MIMO radars. The simulation results demonstrate the effectiveness of the proposed model for enhancing the performance of MIMO radar. Compared with traditional genetic algorithms (GA) and two enhanced GA alternatives, the proposed algorithm achieves a lower ESD loss and better orthogonal performance. Full article

The dead-time effect seriously affects the soft-switching performance and operating efficiency of the dual-active-bridge converter, and also causes problems such as reduced duty cycle, distortion of voltage and current waveforms, and narrowed transmission power range. The proposal of the five-degree-of-freedom modulation strategy transforms the working voltage waveforms of the primary and secondary sides as well as the inductor current waveform of the DAB converter from symmetric to asymmetric, while the dead-time issue still persists. Based on the five-degree-of-freedom modulation strategy, this paper analyzes the electrical characteristics of the converter before and after the introduction of dead time, designs switch drive pulses to avoid the dead time, and proposes a dead-time compensation modulation strategy based on five-degree-of-freedom phase shift. The results show that the proposed dead-time compensation control strategy can avoid problems such as voltage and current waveform distortion and reduction in the soft-switching power range caused by dead time, realizing dead-time compensation in the full power range. Experimental measurements show that, for different voltage transmission ratios, the maximum efficiency improvement is approximately 3.8–4% and the current stress is reduced by 2.11% to 3.13% under low-power operating conditions. The maximum efficiency improvement is approximately about 1.4–2.8% and the current stress is reduced by 1.84% to 2.53% under high-power operating conditions. Full article

We demonstrate coating-free optoacoustic generation and focusing of ultrasound using a mechanically Q-switched (MQS) erbium-doped yttrium aluminum garnet (Er:YAG) source (~100 ns, ≤20 mJ) combined with a concave water interface that simultaneously serves as converter and acoustic lens. Axial, lateral, and focal-point measurements mapped the pressure field while varying beam diameter (2w = 5–15 mm) and pulse energy (E = 10–20 mJ). The maximum focal positive pressure (P_max = 7 MPa) occurs at an intermediate diameter (~10 mm), whereas the tightest lateral/axial confinement and strongest spectral enhancement arise at larger diameters (14–15 mm) with f_c = ~5 MHz and −6 dB bandwidth up to 7 MHz. Pressure increases nearly monotonically with energy. For equal fluence, larger diameters yield higher focal pressures due to greater focusing gain. Small beams (2w ≈ 5–7 mm) show shorter apparent time-of-flight (TOF) and waveform broadening, consistent with early shock-like emission from locally vaporizing region. These results provide practical rules for tuning amplitude, spectrum, and confinement, enabling sub-millimeter focusing for contamination-sensitive and therapeutic applications. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types. Full article

The paper presents experimental results for a modified pulsed plasma thruster (PPT) with solid propellant, using a coaxial anode–cathode design. Graphite from pencil leads served as propellant, and a tungsten trigger electrode was tested to reduce carbonization effects. Experiments were performed in a vacuum chamber at 0.001 Pa, employing diagnostics such as discharge current/voltage recording, power measurement, ballistic pendulum, time-of-flight (TOF) method, and a Faraday cup. Current and voltage waveforms matched an oscillatory RLC circuit with variable plasma channel resistance. Key discharge parameters were measured, including current pulse duration/amplitude and plasma channel formation/decay dynamics. Impulse bit values, obtained with a ballistic pendulum, reached up to 8.5 μN·s. Increasing trigger capacitor capacitance reduced thrust due to unstable “pre-plasma” formation and partial pre-discharge energy loss. Using TOF and Faraday cup diagnostics, plasma front velocity, ion current amplitude, current density, and ion concentration were determined. Tungsten electrodes produced lower charged particle concentrations than graphite but offered better adhesion resistance, minimal carbonization, and stable long-term performance. The findings support optimizing trigger electrode materials and PPT operating modes to extend lifetime and stabilize thrust output. Full article