Search Results (82)

Reliable detection of the rotor winding faults in the doubly fed induction generator (DFIG) is crucial for the resilience of the variable speed energy systems. High-resistance connection (HRC) and inter-turn short circuit (ITSC) faults cause current distortions that are remarkably similar, and the rapid rotor side dynamics and the DFIG multimode operation ability also make fault diagnosis more difficult. This paper proposes a three-layer diagnostic framework named ZSC-CASI-CADI which leverages three-phase rotor currents in conjunction with rotor zero-sequence current (ZSC) for comprehensive rotor winding fault diagnosis. Fault detection is realized through ZSC magnitude and the Cosine Angle Spread Indicator (CASI) enables the strong discrimination between HRC and ITSC faults using the dispersion of rotor current phasors from the ZSC reference. Fault localization is achieved using the Current Angle Difference Indicator (CADI), which determines the faulty rotor phase through the angular deviations in rotor currents from the ZSC. The methodology is verified with extensive simulation results to demonstrate the accurate, real-time fault detection, discrimination, and localization of DFIG rotor winding faults under different load and rotor speed conditions including sub-synchronous and super-synchronous modes. The results show that the proposed framework provides a light and effective solution for rotor winding fault monitoring of the DFIG systems. Full article

Underwater acoustic imaging is essential in marine science and engineering, enabling high-resolution detection and characterization of underwater structures and targets. However, conventional deconvolution beamforming methods using broadband signals often suffer from model mismatch, inter-frequency interference, and limited noise robustness. To overcome these challenges, this study rigorously analyzes the point spread function of the imaging system and introduces Acoustic Frequency Comb (AFC) excitations to enhance resolution. By exploiting the autocorrelation characteristics of AFC signals and optimizing key parameters, imaging artifacts are effectively suppressed and the main-lobe width is narrowed, resulting in a 50% improvement in range resolution. Comparative analyses identify the Richardson–Lucy algorithm as the most effective in enhancing azimuthal resolution and maintaining robustness under array perturbations and low signal-to-noise ratios. Parametric studies further demonstrate that AFC excitation outperforms conventional linear frequency modulated pulses, achieving a 30% main-lobe width reduction, 10 dB sidelobe suppression, and a 14 dB noise decrease. Finally, tank experiments confirm the simulation results, showing that accurate PSF modeling enabled by AFC ensures high angular resolution. The discrete spectral structure facilitates more effective separation of signal and noise during iterative deconvolution, while excellent autocorrelation characteristics guarantee high range resolution, yielding superior overall imaging performance. Full article

Air-to-air (A2A) communication plays a vital role in low-altitude unmanned aerial vehicle (UAV) networks and demands accurate channel modeling to support system analysis and design. A key challenge in A2A channel modeling lies in extracting reliable cluster characteristics, which are often limited due to the scarcity of measurement data. To overcome this limitation, a cluster characteristic analysis method is proposed for UAV A2A channels in built-up environments. First, we reconstruct virtual urban environments, followed by the acquisition of A2A channel data using ray tracing (RT) techniques. Then, a kernel power density (KPD) clustering algorithm is applied to group the multipath components (MPCs). To enhance the modeling accuracy of intra-cluster angular offsets in both elevation and azimuth domains, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is further introduced for generative modeling. A comprehensive analysis is conducted on key cluster characteristics, including the intra-cluster number of MPCs, intra-cluster delay and angular spreads, number of clusters, and angular distributions. The numerical results demonstrate that the proposed WGAN-GP-based approach achieves superior angular fitting accuracy compared to conventional empirical distribution methods. Full article

Terahertz (THz) communication is regarded as a promising technology for future 6G networks because of its advances in providing a bandwidth that is orders of magnitude wider than current wireless networks. However, the large bandwidth and the large number of antennas in THz massive multiple-input multiple-output (MIMO) systems induce a pronounced beam split effect, leading to a serious array gain loss. To mitigate the beam split effect, this paper considers a delay-phase precoding (DPP) architecture in which a true-time-delay (TTD) network is introduced between radio-frequency (RF) chains and phase shifters (PSs) in the standard hybrid precoding architecture. Then, we propose a fast Riemannian conjugate gradient optimization-based alternating minimization (FRCG-AltMin) algorithm to jointly optimize the digital precoding, analog precoding, and delay matrix, aiming to maximize the spectral efficiency. Different from the existing method, which solves an approximated version of the analog precoding design problem, we adopt an FRCG method to deal with the original problem directly. Simulation results demonstrate that our proposed algorithm can improve the spectral efficiency, and achieve superior performance over the existing algorithm for wideband THz massive MIMO systems with angular spread. Full article

With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel characteristics more complex and variable, posing higher requirements for UAV channel modeling. This paper presents a novel predictive channel modeling method based on Transformer architecture by integrating data-driven approaches with UAV air-to-ground channel modeling. By introducing the mmWave and MIMO into UAV communications, the channel data of UAVs at various flight altitudes is first collected. Based on the Transformer network, the typical UAV channel characteristics, such as received power, delay spread, and angular spread, are then predicted and analyzed. The results indicate that the proposed predictive method exhibits excellent performance in prediction accuracy and stability, effectively addressing the complexity and variability of channel characteristics caused by mmWave bands and MIMO technology. This method not only provides strong support for the design and optimization of future 6G UAV communication systems but also lays a solid communication foundation for the widespread application of UAVs in intelligent transportation, logistics, and other fields in the future. Full article

We designed lens systems for a smart-pixel-based optical convolutional neural network (SPOCNN) using optical software to analyze image spread and estimate alignment tolerance for various kernel sizes. The design, based on a three-element lens, was reoptimized to minimize spot size while meeting system constraints. Simulations included root mean square spot and encircled energy diagrams, showing that geometric aberration increases with the scale factor, while diffraction effect remains constant. Alignment tolerance was determined by combining geometric image size with image spread analysis. While the preliminary scaling analysis predicted a limit at a kernel array size of 66 × 66, simulations showed that a size of 61 × 61 maintains sufficient alignment tolerance, well above the critical threshold. The discrepancy is likely due to lower angular aberration in the simulated optical design. This study confirms that an array size of 61 × 61 is feasible for SPOCNN, validating the scaling analysis for predicting image spread trends caused by aberration and diffraction. Full article

Photoacoustic imaging (PAI) has rapidly developed in biomedical imaging. The point spread function (PSF) is critical for addressing image blurring in PAI. However, in circular integrating detection systems, the PSF exhibits spatial variations. This makes PSF extraction challenging. The existing studies typically assume that the PSF is known or obtained through experiments. This study proposes a method for extracting the PSF based on the polar coordinate system. By transforming the image from the Cartesian coordinate system to the polar coordinate system, the “spin blur” problem is decomposed into multiple independent subproblems. With the separation of the radial and angular directions, the blurring kernel remains invariant at each radius, thereby simplifying the estimation of the PSF. To estimate the blurring kernel, we use polynomial algebraic common factor extraction techniques. The numerical simulation results validate the effectiveness of the method, and the impact of sample size on computational efficiency and accuracy is discussed. Full article

We introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X $\in \{\mathrm{M},\mathrm{B},\mathrm{P}\}$ and Y $\in \left\{\mathrm{M}\right\}$—refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments. Full article

Wideband radar is becoming increasingly significant in modern radar systems. However, traditional monopulse angle estimation techniques are not suitable for wideband targets exhibiting range extension effects. To address this, we explore the angle estimation problem for wideband Linear Frequency-Modulated (LFM) signals and propose a new monopulse angle estimation algorithm tailored for range-spread targets. In this paper, the phase of the highest energy scatterer is used as the reference to compensate for the phases of other scatterers. The compensated scatterers are then accumulated for energy focusing. Finally, the angle of the energy-focused signal is estimated using the sum-and-difference amplitude comparison method. The proposed method can effectively focus the scatterers’ energy. Moreover, since the echo of a range-spread target can be regarded as the sum of sinusoids with different frequencies, scatterer energy focusing can effectively improve the performance of the detector. To further demonstrate the practicality of the proposed angle estimation method, it is combined with the detector to evaluate its performance. Simulation results comparing the proposed method with other approaches validate its effectiveness and demonstrate that it achieves a lower signal-to-noise ratio (SNR) threshold and higher angular accuracy. Through the proposed method, tracking and imaging can be achieved entirely within the wideband radar framework. The proposed method can also be extended to other sensor systems, advancing the development of sensor technologies. Full article

To improve the centroid extraction accuracy and efficiency of high-dynamic star sensors, this paper proposes a multi-centroid localization method based on the prior distribution of star trail projections. First, the mapping relationship between attitude information and star trails is constructed based on a geometric imaging model, and an endpoint centroid group extraction strategy is designed from the perspectives of time synchronization and computational complexity. Then, the endpoint position parameters are determined by fitting the star trail grayscale projection using a line spread function, and accurate centroid localization is achieved through principal axis analysis and inter-frame correlation. Finally, the effectiveness of the proposed method under different dynamic scenarios was tested using numerical simulations and semi-physical experiments. The experimental results show that when the three-axis angular velocity reaches 8°/s, the centroid extraction accuracy of the proposed method remains superior to 0.1 pixels, achieving an improvement of over 30% compared to existing methods and simultaneously doubling the attitude measurement frequency. This demonstrates the superiority of this method in high-dynamic attitude measurement tasks. Full article

This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions. Full article

Earlier, a significant enhancement of the nondipole parameters ${\gamma }_{2p}$, ${\delta }_{2p}$, and ${\zeta }_{2p}={\gamma }_{2p}+3{\delta }_{2p}$ in the photoelectron angular distribution for Ne $2p$ photoionization was predicted, owing to resonance interference between dipole ($E1$) and quadrupole ($E2$) transitions. This enhancement manifests as narrow resonance spikes in the parameters due to the low-energy $2s\to 3p$ and $2s\to 4p$ dipole, as well as the $2s\to 3d$ quadrupole autoionizing resonances. Given the unique nature of this predicted enhancement, it requires further validation, specifically regarding whether these narrow spikes in ${\gamma }_{2p}$, ${\delta }_{2p}$ and ${\zeta }_{2p}$ will or will not retain their values for experimental observation if one accounts for a typical finite frequency spread in the ionizing radiation. To address this, we revisit the previous study, now incorporating the effect of frequency spread in the ionizing radiation, assuming a spread as large as 5 meV at the half-maximum of the radiation’s intensity. In the present paper we demonstrate that while the frequency spread does affect the resonance enhancement of ${\gamma }_{2p}$, ${\delta }_{2p}$ and ${\zeta }_{2p}$, these parameters still retain quantitatively significant values to be observed experimentally. The corresponding calculations were performed using the random phase approximation with exchange, which accounts for interchannel coupling in both dipole and quadrupole photoionization amplitudes. Full article

Synthetic aperture interferometry (SAI) is a signal processing technique that mixes the signals collected by pairs of elementary antennas to obtain high-resolution images with the aid of a computer. This note aims at studying the effects of the distance between the synthetic aperture interferometer and an observed scene with respect to the size of the antenna array onto the imaging capabilities of the instrument. Far-field conditions and near-field ones are compared from an algebraic perspective with the aid of simulations conducted at microwave frequencies with the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) mission. Although in both cases the signals kept by pairs of elementary antennas are cross-correlated to obtain complex visibilities, there are several differences that deserve attention at the modeling level, as well as at the imaging one. These particularities are clearly identified, and they are all taken into account in this study: near-field imaging is investigated with spherical waves, without neglecting any terms, whereas far-field imaging approximation is considered with plane waves according to the Van–Citter Zernike theorem. From an algebraic point of view, although the corresponding modeling matrices are both rank-deficient, we explain why the singular value distributions of these matrices are different. It is also shown how the angular synthesized point-spread function of the antenna array, whose shape varies with the distance to the instrument, can be helpful for estimating the boundary between the Fresnel region and the Fraunhofer one. Finally, whatever the region concerned by the aperture synthesis operation, it is shown that the imaging capabilities and the performances in the near-field and far-field regions are almost the same, provided the appropriate modeling matrix is taken into account. Full article

Cu–Al alloys are widely used in electronics, new energy, and other fields due to the combination of th excellent corrosion resistance and electrical conductivity of Cu and the light weight of Al. In this paper, the powder metallurgy and equal-channel angular pressing compound technology was used to fabricate a Cu–Al alloy joint, which can be used to replace armor. Devices such as an optical microscope, electron scanning microscope, and microhardness scale were used to characterize the microstructure and mechanical properties of the Cu–Al alloys. The finite element analysis software Abaqus was used to analyze stress distribution during equal-channel angular pressing. The results indicated that the microstructure and properties of Cu–Al alloys were closely related to the volume ratio of Cu–Al. The microhardness and tensile strength were significantly increased by increasing the volume ratio of Cu–Al. As the volume ratio of Cu–Al varied from 1:2 to 2:1, the ultimate tensile strength of the Cu–Al alloys increased from 79.9 MPa to 164.9 MPa at room temperature and the microhardness increased from 60 HV to 101 HV. However, the elongation of the Cu–Al alloys hardly changed; this was about 4.4%. Crack initiation occurred at the Cu–Al interface and spread along the bonding surface of the Cu–Al alloys during the tensile process. Full article

Recent developments in ultrashort and intense laser systems have enabled the generation of short and brilliant proton sources, which are valuable for studying plasmas under extreme conditions in high-energy-density physics. However, developing sensors for the energy selection, focusing, transport, and detection of these sources remains challenging. This work presents a novel and simple design for an isochronous magnetic selector capable of angular and energy selection of proton sources, significantly reducing temporal spread compared to the current state of the art. The isochronous selector separates the beam based on ion energy, making it a potential component in new energy spectrum sensors for ions. Analytical estimations and Monte Carlo simulations validate the proposed configuration. Due to its low temporal spread, this selector is also useful for studying extreme states of matter, such as proton stopping power in warm dense matter, where short plasma stagnation time (<100 ps) is a critical factor. The proposed selector can also be employed at higher proton energies, achieving final time spreads of a few picoseconds. This has important implications for sensing technologies in the study of coherent energy deposition in biology and medical physics. Full article