Search Results (174)

The bridge arms and DC voltage of China’s Four-Terminal Flexible DC Transmission Project exhibit persistent high-frequency harmonics over the medium to long term, causing issues such as overheating losses and electromagnetic interference within the converter stations. To address this issue, this paper first introduces the structure of the Four-Terminal Flexible DC Grid and the high-frequency harmonic characteristics on the DC side, clarifying the impact of control cycles on the harmonic distribution at converter stations. Through analysis of the modulating wave, it is demonstrated that the sideband harmonics originate from the coupling effect between the control cycle and the modulating wave, inducing high-frequency sideband harmonics on the bridge arm. A discrete switching equation for bridge arm voltage was established. Based on double Fourier decomposition, a mathematical model for sideband harmonics was derived, and the flow direction of these harmonics was analyzed. A four-terminal flexible DC system was constructed using PSCAD electromagnetic transient simulation, yielding harmonic distributions in the arm and DC-side sidebands. This validated the accuracy of theoretical analysis and ultimately identified the factors influencing sideband harmonics. Full article

The possibility of developing autonomous vessels has recently become increasingly interesting. As most vessels are powered by diesel engines, the idea of developing a method to detect engine malfunctions by analyzing signals from microphones placed near the engine and accelerometers mounted on the engine housing is intriguing. This paper presents a method for detecting engine malfunctions by analyzing signals obtained from the output of a microphone and accelerometer. The algorithm is based on signal analysis in the frequency domain using discrete Fourier transform (DFT), and the same procedure is applied to both acoustic and vibration data. The proposed method was tested on a six-cylinder marine diesel engine where a fault was emulated by deactivating one cylinder. In controlled experiments across five rotational speeds, the method achieved an accuracy of approximately 98.3% when trained on 75 operating cycles and evaluated over 15 cycles. The average precision and recall across all sensors exceeded 97% and 96%, respectively. The ability of the algorithm to treat microphone and accelerometer signals identically simplifies implementation, and the detection accuracy can be increased further by adding additional sensors. Full article

Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article

This study investigates the influence of particle morphology on two-dimensional (2D) single rock particle breakage using the combined finite-discrete element method (FDEM) coupled with fractal dimension analysis. Three key shape descriptors (elongation index EI, roundness index Rd, and roughness index Rg) were systematically varied to generate realistic particle geometries using the Fourier transform and inverse Monte Carlo. Numerical uniaxial compression tests revealed distinct morphological influences: EI showed negligible impact on crushing strength or fragmentation, and Rd significantly increased crushing strength and fragmentation due to improved energy absorption and stress distribution. While Rg reduced strength through stress concentration at asperities, suppressing fragmentation and elastic energy storage. Fractal dimension analysis demonstrated an inverse linear correlation with crushing strength, confirming its predictive value for mechanical performance. The validated FDEM framework provides critical insights for optimizing granular materials in engineering applications requiring morphology-controlled fracture behavior. Full article

The harmonic balance method (HBM) is a widely used method for determining the forced response of non-linear systems such as bladed disks. This paper focuses on analyzing the sensitivity of this method to key computational parameters and its robustness. HBM and HBM coupled with pseudo arc length continuation are used in this paper to solve the equation of motion of a test case. The pseudo arc length continuation is necessary because when intermittent contact occurs, natural continuation cannot guarantee solver convergence. Intermittent contact, in addition to turning points, introduces further problems, which are caused by an infinite sequence of decaying, but not zero, Fourier coefficients. This results in the need to oversample the non-linear force time signal to avoid convergence problems. The computational parameters investigated in this paper are the samples per period, which determine the number of points in which the time signal is discretized, and the harmonic truncation order. In addition, the connection of contact parameters, such as friction and contact stiffness, with computational parameters is analyzed. This study shows that the number of time samples per period is the most limiting parameter when intermittent contact occurs; whereas, in the absence of intermittent contact convergence, problems can be avoided with a reasonable number of time points. Poor discretization of the signal leads to a bad computation of Fourier coefficients and thus a lack of convergence. Sensitivity analysis shows that the samples per period depend on the contact parameters, especially normal stiffness. To ensure the solver robustness, it is important to set the computation parameters appropriately to ensure the convergence of the solver while avoiding unnecessary computation effort. Full article

Electro-Mechanical Braking (EMB), as a novel brake-by-wire technology, is rapidly being implemented in vehicle chassis systems. Nevertheless, the integrated design of the EMB caliper contributes to an increased unsprung mass in Distributed Drive Electric Vehicles (DDEVs). Experimental results indicate that when the Anti-lock Braking System (ABS) is activated, these factors can induce high-frequency wheel oscillations. To address this issue, this study proposes an anti-oscillation control strategy tailored for EMB systems. Firstly, a quarter-vehicle model is established that incorporates the dynamics of the drive motor, suspension, and tire, enabling analysis of the system’s resonant behavior. The Discrete Fourier Transform (DFT) is applied to the difference between wheel speed and vehicle speed to extract the dominant frequency components. Then, an Adaptive Braking Intensity Field Regulation (ABIFR) strategy and a Model Predictive and Logic Control (MP-LC) framework are developed. These methods modulate the amplitude and frequency of braking torque reductions executed by the ABS to suppress high-frequency wheel oscillations, while ensuring sufficient braking force. Experimental validation using a real vehicle demonstrates that the proposed method increases the Mean Fully Developed Deceleration (MFDD) by 14.8% on low-adhesion surfaces and 15.2% on high-adhesion surfaces. Furthermore, the strategy significantly suppresses 12–13 Hz high-frequency oscillations, restoring normal ABS control cycles and enhancing both braking performance and ride comfort. Full article

Epileptic seizure prediction remains a critical challenge in neuroscience and healthcare, with profound implications for enhancing patient safety and quality of life. In this paper, we introduce a novel seizure prediction method that leverages electroencephalogram (EEG) data, combining discrete wavelet transform (DWT)-based time–frequency analysis, advanced feature extraction, and deep learning using Fourier neural networks (FNNs). The proposed approach extracts essential features from EEG signals—including entropy, power, frequency, and amplitude—to effectively capture the brain’s complex and nonstationary dynamics. We measure the method based on the broadly used CHB-MIT EEG dataset, ensuring direct comparability with prior research. Experimental results demonstrate that our DWT-FS-FNN model achieves a prediction accuracy of 98.96 with a zero false positive rate, outperforming several state-of-the-art methods. These findings underscore the potential of integrating advanced signal processing and deep learning methods for reliable, real-time seizure prediction. Future work will focus on optimizing the model for real-world clinical deployment and expanding it to incorporate multimodal physiological data, further enhancing its applicability in clinical practice. Full article

Based on the new high-resolution multibeam bathymetry data collected by the “Dongfanghong 3” vessel in 2023 in the Parece Vela Basin (PVB) and previous magnetic anomaly data, we systematically analyze the seafloor topographical changes of abyssal hills and oceanic core complexes (OCCs) in the “Chaotic Terrain” region, and the revised seafloor spreading model is constructed in the PVB. Using detailed analysis of the seafloor topography, we identify typical geomorphological features associated with seafloor spreading, such as regularly aligned abyssal hills and OCCs in the PVB. The direction variations of seafloor spreading in the PVB are closely related to mid-ocean ridge rotation and propagation. The formation of OCCs in the “Chaotic Terrain” can be explained by links to the continuous and persistent activity of detachment faults and dynamic adjustments controlled by variations of deep magma supply in the different segments in the PVB. We use 2D discrete Fourier image analysis of the seafloor topography to calculate the aspect ratio (AR) values of abyssal hills in the western part of the PVB. The AR value variations reveal a distinct imbalance in magma supply across various regions during the basin spreading process. Compared to the “Chaotic Terrain” area, the region with abyssal hills indicates a higher magma supply and greater linearity on seafloor topography. AR values fluctuated between 2.1 and 1.7 of abyssal hills in the western segment, while in the “Chaotic Terrain”, they dropped to 1.3 due to the lower magma supply. After the formation of the OCC-1, AR values increased to 1.9 in the eastern segment, and this shows the increase in magma supply. Based on changes in seafloor topography and variations in magma supply across different segments of the PVB, we propose that the seafloor spreading process in the magnetic anomaly linear strip 9-6A of the PVB mainly underwent four formation stages: ridge rotation, rift propagation, magma-poor supply, and the maturation period of OCCs. Full article

The growing demand for the production of food has led to the development of new analytical techniques in the food industry, enabling innovative strategies to streamline food production and ensure its physicochemical and microbiological quality. In this work, a smart sensor was developed using the electrical impedance spectroscopy (EIS) technique. The system is based on discrete Fourier transform (DFT) and incorporates a Howland current source. The experimental results showed that the sensor was able to detect the moisture content in potatoes (Solanum tuberosum L.). Favorable responses were obtained by exciting the system with two frequency intervals: 0–100 Hz and 500–5000 Hz. An exhaustive analysis of the frequency response was performed to identify the most linear behavior in the moisture measurement, with an R-squared of 0.786 and signals in intervals from 500 to 5000 Hz. Moreover, the linearity remained stable across most frequencies, resulting in consistent measurements, even with the implementation of low-cost components. Full article

In this paper, we propose a high-order compact difference scheme for a class of time-fractional convection–diffusion–reaction equations (CDREs) with variable coefficients. Using the Lagrange polynomial interpolation formula for the time-fractional derivative and a compact finite difference approximation for the spatial derivative, we establish an unconditionally stable compact difference method. The stability and convergence properties of the method are rigorously analyzed using the Fourier method. The convergence order of our discrete scheme is $\mathcal{O}({\tau }^{4-\alpha }+h^8)$, where $\tau$ and h represent the time step size and space step size, respectively. This work contributes to providing a better understanding of the dependability of the method by thoroughly examining convergence and conducting an error analysis. Numerical examples demonstrate the applicability, accuracy, and efficiency of the suggested technique, supplemented by comparisons with previous research. Full article

Conventional public-key cryptographic systems are increasingly threatened by advances in quantum computing, accelerating the need for robust post-quantum cryptographic solutions. Among these, Falcon, a compact lattice-based digital signature scheme, has emerged as a leading candidate in the NIST post-quantum standardization process due to its efficiency and theoretical security grounded in hard lattice problems. This work introduces Falcon-M, a modified version of the Falcon algorithm that significantly reduces implementation complexity. It does so by replacing Falcon’s intricate trapdoor-based key-generation mechanism with a simplified approach that utilizes randomized polynomial Gaussian sampling and fast Fourier transform (FFT) operations. Falcon-M incorporates SHA-512 hashing and discrete Gaussian sampling to preserve cryptographic soundness and statistical randomness while maintaining the core structure of Falcon’s signing and verification processes. We formally specify the Falcon-M algorithm, provide an updated pseudocode, and offer a comparative analysis with the original Falcon in terms of algorithmic complexity, security assumptions, and implementation overhead. Additionally, we present formal lemmas and theorems to ensure correctness and define theoretical bounds on forgery resistance. Although Falcon-M does not rely on a formal cryptographic trapdoor, we demonstrate that it achieves strong practical security based on assumptions related to the Short Integer Solution (SIS) problem. Falcon-M is thus well-suited for lightweight post-quantum applications, particularly in resource-constrained environments, such as embedded systems and Internet-of-Things (IoT) platforms. Full article

We present an analytical framework for describing light propagation in infinite waveguide arrays, incorporating a generalized long-range coupling to achieve a more realistic model. We demonstrate that the resulting solution can be expressed in terms of generalized Bessel-like functions. Additionally, by applying the concept of eigenstates, we borrow from quantum mechanics a basis given in terms of phase states that allows the analysis of the transition from the discrete to the continuum limit, obtaining a relationship between the field amplitudes and the Fourier series coefficients of a given function. We apply our findings to different coupling functions, providing new insights into the propagation dynamics of these systems. Full article

With the advancement of spatial positioning technology, trajectory data have been growing rapidly. Trajectory data record the spatiotemporal information and behavioral characteristics of moving objects, and in-depth analysis can provide decision support for urban transportation. This paper explores effective methods for trajectory data representation, with a focus on the study of data fitting methods. Data fitting can extract key information and reveal underlying patterns, and the use of fitting methods can significantly improve the efficiency and accuracy of spatiotemporal trajectory data analysis, offering new perspectives and methodological support for related research fields. This paper integrates road network data to enhance trajectory data, treating trajectory data as a dynamic signal that changes over time. Through Fourier transformation, the data are converted from the time domain to the frequency domain, and trajectory points are fitted in the frequency spectrum domain, transforming discrete trajectory points into time-continuous linear elements. By referencing the minimum visually discernible distance and velocity precision requirements at a specific scale, thresholds for positional and velocity errors are set. The similarity between the Fourier-fitted trajectory and the original trajectory is measured in both spatial and temporal dimensions. By calculating the number of expansion terms of the Fourier series at a specific spatiotemporal scale, a functional relationship between the number of expansion terms, duration, and distance is fitted within the set threshold range (R² = 0.8424). This enables the Fourier series representation of any trajectory data under specific positional and velocity error thresholds. The errors in position and velocity obtained using this expression are significantly lower than the theoretical errors. The experimental results demonstrate that the Fourier fitting method exhibits strong generality and precision, effectively approximating the original trajectory, and provides a robust mathematical foundation for the quantification and detailed analysis of trajectory data. Full article

This paper presents a discrete Fourier transform (DFT)-based signal processing framework for eddy current non-destructive testing (NDT), aiming to enhance signal quality for precise defect characterization in critical nuclear components. By enforcing strict periodicity matching between sampling points and signal frequencies, the proposed approach mitigates DFT spectrum leakage, validated via phase linearity analysis with errors of ≤0.07° across the 20 Hz–1 MHz frequency range. A high-resolution 24-bit analog-to-digital converter (ADC) hardware architecture eliminates complex analog balancing circuits, reducing system-wide noise by overcoming the limitations of traditional 16-bit ADCs. A 6 × 6 mm application-specific integrated circuit (ASIC) for array sensors enables three-dimensional (3D) defect visualization, complemented by Gaussian filtering to suppress vibration-induced noise. Our experimental results demonstrate that the digital method yields smoother signal waveforms and superior 3D defect imaging for nuclear power plant tubes, enhancing result interpretability. Field tests confirm stable performance, showcasing clear 3D defect distributions and improved inspection performance compared to conventional techniques. By integrating DFT signal processing, hardware optimization, and array sensing, this study introduces a robust framework for precise defect localization and characterization in nuclear components, addressing key challenges in eddy current NDT through systematic signal integrity enhancement and hardware innovation. Full article

The structural stability of transmission towers critically depends on the integrity of bolted connections, necessitating accurate bolt loosening detection for power grid safety. Traditional methods, such as manual inspection and hammering-based auditory analysis, suffer from inefficiency and inaccuracy due to environmental noise and subjective judgment. This paper proposes a novel machine learning framework for intelligent bolt loosening detection using acoustic signature analysis. The framework integrates multi-channel acoustic data from strategically placed sensors, extracting Mel-Frequency Cepstral Coefficients (MFCCs) through pre-emphasis, framing–windowing, the Fourier transform, Mel-filter bank processing, and the discrete cosine transform. Adversarial training is employed to suppress noise interference and hammering force variability by augmenting training data with perturbed samples. Experimental validation on 110 kV and 220 kV transmission towers demonstrates the framework’s efficacy: the Support Vector Machine (SVM) achieves 89.93% accuracy, 86.26% precision, 84.89% recall, and 84.91% F1 score, outperforming Decision Tree (86.7% accuracy), K-Nearest Neighbors (89.0%), Random Forest (84.86%), and XGBoost (89.47%). The proposed solution enables reliable, scalable bolt loosening detection, significantly advancing intelligent maintenance for power transmission infrastructure. Full article