Fast Fourier Transform Method Research Articles

One-Minute Quasi-Periodic Pulsations during an M-Class Solar Flare

We study the Quasi-Periodic Pulsations (QPPs) of an M4.4 class solar flare, which occurred in active region NOAA 11165 on 8 March 2011. With the Fast Fourier Transform (FFT) method, we decompose the flare light curve into fast- and slowly-varying components. The 100 s (0.01 Hz) is selected as the cutoff threshold between the fast- and slowly-varying components. One-minute QPPs are found around flare peak at soft X-ray (SXR) and Extreme Ultraviolet (EUV). Using the data from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), the intermittent jets are detected and the interesting fact is that the jets also display one-minute period. The correlationship between the fast-varying components of SXR or EUV emissions and the jets suggests that the QPPs on light curves and periodic jets could come from the same origination, e.g., the periodic magnetic reconnection in this event.

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Physical disability due to amputation can affect a person's quality of life due to limited movement in performing daily activities. Bionic hands are used to help someone with an amputation disability. This research developed a bionic hand control based on electroencephalography sensors capable of measuring the brain's bioelectric activity. The classified brain wave was then translated as activity pattern information. The alpha & beta waves were the focus of this work. This study demonstrated a method to extract and classify motor imagery of brainwave activity patterns. The Fast Fourier Transform (FFT) method extracts motor imagery characteristics. The extraction of features is then classified by the Multilayer Perceptron (MLP) method for five classes of bionic hand movement. Testing was conducted with two scenarios. The first test motor imagery without additional movement showed an accuracy of 77.20 %, while the second test motor imagery combined with head movement showed an accuracy of 84.40% for five classes. The system based on motor imagery has been implemented in a bionic hand that shows the applicability of the proposed method.

Short-time and weak signal demodulation for fiber optic current sensors based on backpropagation neural network

It has been challenging to demodulate short-time and weak current signals collected by fiber optic current sensors (FOCSs) under ultra-high voltage, since the background noise can significantly affect the spectra of the current signals. To address this issue, here we propose a novel FOCS demodulation method based on backpropagation neural network, where the impact of the noise on the measurement can be largely reduced. The demodulation method can determine the amplitudes of short time series of weak currents with high resolution and accuracy. To evaluate the performance of the proposed method, the demodulation accuracy and robustness of the method are experimentally investigated and compared with those of the traditional fast Fourier transform (FFT) method. The experimental results show that our method can produce reliable results when demodulating weak current signals over short time windows of less than one period, and achieve a significantly lower standard deviation (6.7 mA) compared with the FFT method (15.6 mA) in the current range 0–0.306 A. The higher robustness against the background noise than the FFT method and the excellent repeatability of our demodulation method are also demonstrated through simulations. These results suggest that the proposed method will provide an effective way to improve the detection performances of FOCSs in fast dynamic measurements.

FFT, DA, and Mori-Tanaka approximation to determine the elastic moduli of three-phase composites with the random inclusions

In this work, some solutions such as Mori-Tanaka approximation (MTA), Differential approximations (DA), and Fast Fourier transformation method (FFT) were applied to estimate the elastic bulk and shear modulus of three-phase composites in 2D. In which two different sizes of circular inclusions are arranged randomly non-overlapping in a continuous matrix. The numerical solutions using FFT analysis were compared with DA, MTA, and Hashin-Strikman's bounds. The MTA and DA reasonably agreeable solution with the FFT solution shows the effectiveness of the approximation methods, which makes MTA, DA useful with simplicity and ease of application.

Dependence of thermomagnetic instability on strong nonlinear <i>E</i>-<i>J</i> models in superconducting films

The <inline-formula><tex-math id="M4">\begin{document}$E\text{-}J$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M4.png"/></alternatives></inline-formula> relationship in conventional conductor generally satisfies the linear Ohm's law. However, the <inline-formula><tex-math id="M5">\begin{document}$E\text{-}J$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M5.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M5.png"/></alternatives></inline-formula> model in superconductors presents strong nonlinear characteristics, which is significantly different from that of the conventional conductor. According to the nonlinear <inline-formula><tex-math id="M6">\begin{document}$E\text{-}J$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M6.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M6.png"/></alternatives></inline-formula> power law of superconducting materials, we quantitatively investigate the relationship between the magnetic-thermal stability and the nonlinear constitutive characteristic of superconducting films at different temperatures, magnetic field ramp rates, and critical current densities by using the fast Fourier transform method (FFT). We find that the strong nonlinear electromagnetic constitutive model plays a crucial role responsible for the onset and morphology (tree-like and finger-like) of the magneto-thermal instability of superconducting thin films. In addtion, the reason why similar magneto-thermal instabilities cannot be observed in conventional conductors is also explained. It can be found that the magnetic field on the border of the superconducting film increases rapidly for a larger creep exponent due to the enhancement of diamagnetism, which results in a large magnetic pressure and easily triggering off flux avalanches. Therefore, the threshold field of flux avalanches in the superconducting film decreases with flux creep exponent increasing. Finally, we present the curves that can clearly divide the <inline-formula><tex-math id="M7">\begin{document}$n_0\text{-}j_{c0}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M7.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M7.png"/></alternatives></inline-formula> plane and <inline-formula><tex-math id="M8">\begin{document}$n_0\text{-}\dot {H}_a$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M8.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="20-20220285_M8.png"/></alternatives></inline-formula> plane into magneto-thermal stability region and magneto-thermal instability region for superconducting thin film with different levels of nonlinearity.

Compressor Stability at Pulsating Condition Via Gas Dynamics Responses Analysis

Abstract The compressor is operated at unsteady exit conditions in many scenarios such as the instability of combustion in gas turbine and denotation engine. The aerodynamic stability of the compressor is inevitably influenced by the unsteady operating environment. In this paper, a one-dimensional (1D) model that combines pulsation and unsteady responses of the compressor is developed to investigate the stability of the compressor system. The results show that the onset of surge and the transient responses of the compressor during surge are well predicted by the method. The amplitude of the component at surge frequency obtained by fast Fourier transformation (FFT) method is applied as a criterion to quantitively evaluate the onset and the strength of the surge. Compressor stability is enhanced at pulsating conditions due to the interaction between forced oscillation of pulsation and self-sustained oscillation of surge. An analytical model is established to understand the mechanism of the enhancement of compressor stability under pulsation. The change of total energy of the compressing system is proposed to evaluate the influence of pulsating conditions. Specifically, the energy change is reduced for pulsation conditions due to two aspects: the lower slope of compressor characteristic curve due to the lag-effect of compressor responses and the higher energy dissipation due to the nonlinear throttling effect.

Efficient two-scale analysis with thermal residual stresses and strains based on self-consistent clustering analysis

The thermal residual stresses and strains are crucial in the industrial manufacturing process of the metal alloys and other composites. In representative volume element (RVE) of these complex composite structures, how to accurately and efficiently simulate the responses considering thermal residual stresses and strains is also vital, which could be served as a foundation to multiscale analysis. Recently an effective reduced-order method named self-consistent clustering analysis (SCA) has been proposed to solve microscopic responses in RVE. This paper modifies the original SCA method for thermoelastic and thermal elastoplastic problems, and the microscopic responses of the RVE are compared with the results of analytical and Fast Fourier Transform (FFT) methods to validate its accuracy and feasibility. Then, this study demonstrates the influences of different clustering strategies in offline stage, develops the clustering number optimization and discusses the two-scale analysis using the modified SCA method with/without thermal residual stresses and strains in microscale. In two-scale analysis, the macroscopic and microscopic responses are studied to explore the effects of thermal residual stresses and strains. The two-scale analysis framework with modified SCA method proposed in this work is proved to be efficient in simulating composites considering thermal residual strains and stresses.

Inrush Current Based on Fast Fourier Transform

Advances in technology have caused the use of electricity to increase rapidly. With advances in technology, this is followed by the use of increasingly efficient electrical components or equipment. This more efficient electrical equipment causes the impedance of the component to be smaller, causing a surge in current when it is turned on. This current surge, if not followed by appropriate safety precautions, will be damage other components. Each load has different waveform characteristics and current transient peaks. For this reason, it is necessary to analyze the transient condition of a load to overcome this. This paper will explain the characteristics of the inrush current of the load due to ignition. There are three loads used in this study, namely resistive, capacitive and inductive loads. Then the use of this load is simulated by giving different ignition angle values, namely 0, 60, and 90 degrees. The analysis used is the Fast Fourier Transform (FFT) method which is a derivative of the Discrete Fourier Transform. The inrush current spectrum in this simulation is simulated using Simulink MATLAB with switching system modeling using TRIAC. This inrush current simulation data collection uses a sampling frequency of 100 Khz and will be analyzed in the first of 5 cycles. For each load in this paper, the harmonic values for each ignition angle will be presented. The simulation results show that the inrush current is caused by the ignition angle value used and because of components that can deviate energy such as inductors and capacitors as well as components which at the time of starting have a low impedance value such as incandescent lamps. The simulation also shows that the use of switching components for setting the ignition angle causes an increase in the value of Total Harmonic Distortion (THD) but the peak current in the first cycle when the ignition angle is set decreases.

Influence of Confinement Pressure on the Mechanical Response of Emulsified Cold-Recycled Mixtures

Cold recycling of asphalt pavement is an effective rehabilitation technique in terms of saving natural resources and cost of construction. The current study focuses on cold-recycled asphalt mixture designated as emulsified cold-recycled mixture (ECRM) that contains 100% reclaimed asphalt pavement material and slow-setting asphalt emulsion. Here, a repeated haversine compression test is conducted with and without confinement pressure at six temperatures (0°C, 10°C, 20°C, 30°C, 40°C, and 50°C) and 10 frequencies (25, 20, 10, 5, 2, 1, 0.5, 0.2, 0.1, and 0.01 Hz). The stress–strain–time response of ECRM is investigated, and it is observed that the influence of confinement pressure is negligible in the temperature range of 0°C–10°C, whereas it is significant at temperature 30°C and higher, irrespective of the frequency of loading. However, at 20°C, the influence of confinement pressure is negligible at higher frequencies (25–10 Hz), and it is significant at lower frequencies (less than 10 Hz). The viscoelastic response of ECRM is seen at all temperature, frequency, and confinement conditions of loading from the analyzed Lissajous plots. The dynamic modulus and phase angle are computed using the regression and the fast Fourier-transform (FFT) methods. The dynamic modulus and phase angle master curve is then generated using a free-shifting approach and a model-based approach. The model-based approach involves the numerical solution of the exact Kramers–Kronig relations combined with the generalized logistic sigmoidal model. The FFT-based dynamic modulus is used to construct a master curve with the generalized logistic sigmoidal model. Such a model-based master curve resulted in excellent predictive capabilities for dynamic modulus, and hence it is suggested for ECRM.

A Spatially Distributed Network for Tracking the Pulsation Signal of Flow Field Based on CFD Simulation: Method and a Case Study

The pulsating characteristics in turbulent flow are very important physical quantities. There are many studies focused on the temporal characteristics of pulsation. However, the spatial distribution of temporal states with pulsations rarely receives attention. Therefore, the pulsation tracking network (PTN) method is proposed to track the pulsating characteristics of turbulence. Based on the computational fluid dynamics (CFD) simulation result, the PTN is arranged in a specific region of the flow domain. The fast Fourier Transform (FFT) method is used for time-frequency conversion. As shown in the example of trailing-edge vortex-shedding flow over NACA0009 hydrofoil, important pulsation quantities, including the total pulsation intensity, dominant frequencies, amplitude of frequencies, and the phase and phase difference, can be obtained with a high spatial resolution. The source, reason and attenuation of the vortex-shedding frequency fvs and the 2 fvs frequency caused by vortex-interaction are well indicated. The dominant regions of fvs and 2 fvs are shown and analysed. The propagation and attenuation of vortex-shedding induced pulsation are understood in detail. Based on the comparison against traditional analysis, PTN is found to function as a good supplement for the CFD post-processing by tracking unknown temporal and spatial characteristics. These findings represent a potential breakthrough in terms of solving actual pulsation-excited flow problems.

Novel method of multi-frequency flicker to stimulate SSVEP and frequency recognition

This work uses the multi-frequency flicker to stimulate SSVEP, which is different from earlier studies that using single-frequency flicker as flash stimulators. Usually, the frequency recognition of SSVEP were converted into command output with the widely used Fast fourier transform (FFT). In the experiment, seven subjects stared at the six groups of LED stimulators with two or more different and independent flicker frequencies. Three electrodes placed at Oz-A1 and Fpz (Ground) used to measure Electroencephalography (EEG) signals. After processing the EEG signal, the frequency recognition of SSVEPs were converted into command output with the FFT and the proposed novel method (NM). The results of 28-second and every 4-second segmented EEG signals were interfered by external noise that cannot to recognize the correct signals after FFT processing. On the contrary, these unrecognizable signals can clearly recognized the maximum ρ value after using NM processing. In addition, the combinations of multi-frequency flicker provide the more stimulation options to apply to BCI control. This work uses only one channel of the EEG signal and the proposed novel method (NM) is simpler and have higher recognition rate than widely used FFT method.

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

Pulse rate estimation from forehead photoplethysmograph signal using RLS adaptive filtering with dynamical reference signal

Objective: Pulse rate (PR) extracted from Photoplethysmograph (PPG) is a significant feature in emotion recognition. However, PPG signal can be easily affected by the motion artifacts (MA) produced from the body movement and the random head swing of the subjects who are in Immersive Virtual Environments (IVE). The objective is to estimate PR from MA corrupted PPG signal.Methods: In order to reduce the influence of random MA, we propose a novel adaptive noise cancellation (ANC) method. This method could attenuate MA and reduce the execution time by adopting RLS adaptive filtering with dynamical reference signal (DRS-RLS). Then the spectral peak tracking with verification based on the Fast Fourier Transform (FFT) method is used to estimate PR value.Results: In this paper, the two-channel PPG signals and tri-axis acceleration signals were recorded synchronously from forehead by the virtual reality (VR) physiological helmet platform developed in our research center. Seventeen participants were able to observe and move randomly in four IVEs. The mean average absolute error (μAAE) using our proposed PR estimation method is 2.40 beats per minute (BPM). Furthermore, verification result of the proposed method on a public database of 22 PPG recordings showed the μAAE (2.01 BPM) and runtime (17.2 ms) of each time window.Conclusion: The proposed PR estimation method has achieved the lowest error compared with RLS adaptive filtering using fixed axis as reference signal or cascade RLS adaptive filtering. And the result has also shown a lower μAAE and less runtime than other adaptive filtering methods proposed in recent years.Significance: The proposed method in this paper has potential to be implemented in wearable devices due to its low error and low computation cost. It also provides a new way for reference signal selection of adaptive filtering.

Improving the Performance of Steel Machining Processes through Cutting by Vibration Control.

Machining processes through cutting are accompanied by dynamic phenomena that influence the quality of the processed surfaces. Thus, this research aimed to design, make, and use a tool with optimal functional geometry, which allowed a reduction of the dynamic phenomena that occur in the cutting process. In order to carry out the research, the process of cutting by front turning with transversal advance was taken into account. Additionally, semi-finished products with a diameter of Ø = 150 mm made of C45 steel were chosen for processing (1.0503). The manufacturing processes were performed with the help of two tools: a cutting tool, the classic construction version, and another that was the improved construction version. In the first stage of the research, an analysis was made of the vibrations that appear in the cutting process when using the two types of tools. Vibration analysis considered the following: use of the Fast Fourier Transform (FFT) method, application of the Short-Time Fourier-Transformation (STFT) method, and observation of the acceleration of vibrations recorded during processing. After the vibration analysis, the roughness of the surfaces was measured and the parameter Ra was taken into account, but a series of diagrams were also drawn regarding the curved profiles, filtered profiles, and Abbott–Firestone curve. The research showed that use of the tool that is the improved constructive variant allows accentuated reduction of vibrations correlated with an improvement of the quality of the processed surfaces.

Fast numerical simulation of 2D gravity anomaly based on nonuniform fast Fourier transform in mixed space-wavenumber domain

Wavenumber domain gravity modeling is typically based on the fast Fourier transform (FFT), requiring uniform sampling in both space and wavenumber domains and grid expansion to guarantee the accuracy. In this paper, we develop an efficient and highly accurate 2D forward modeling algorithm based on the nonuniform fast Fourier transform (NUFFT) in the hybrid space and wavenumber domain. To retain the accuracy of space domain methods, the Fourier transform is carried out only in the horizontal direction based on NUFFT and no transform is applied in the vertical direction (make the discretization in the vertical direction flexible). Thus, the original gravity modeling problem is transformed into a sequence of independent space domain 1D integral equations, which can be solved in parallel efficiently. For the 1D integral in the space domain, the density is represented by quadratic shape function and the independent equations can be solved analytically. The application of the NUFFT algorithm allows for the nonuniform discretization of a model and improves calculation efficiency while ensuring calculation accuracy. Based on a 2D prism model, the accuracy of the proposed algorithm is verified and its numerical performance in terms of efficiency and accuracy is compared with the standard FFT and Gauss-FFT methods, indicating that at the same accuracy our algorithm is most efficient. Then, three more complex models and one BP density model are designed to further demonstrate the good numerical performance of the proposed algorithm.

Automatic Bayesian modal identification method for structures based on blind source separation

ABSTRACT An automatic Bayesian modal identification method is proposed using the blind source separation (BSS) technique. The determination of resonant frequency bands, which is the initial step of the fast Bayesian FFT (fast Fourier transform) method, requires human intervention and hence, is labour-intensive and subjective. To automate the determination of resonant frequency bands, the BSS technique is introduced here for band selection process. After estimating the modal responses from measured data, the hump criterion curves are drawn to sharpen the border of the resonant humps. And the frequency bands can thus be determined automatically by locating the resonant humps with a peak picking algorithm. The proposed method was validated with a simulated 6- degree-of-freedom spring-mass model, a simulated 4-story benchmark model, the Heritage Court Tower in Vancouver, Canada. The robust identification results indicate that the proposed method can identify automatically and accurately the physical modes together with their uncertainty.

Numerical methodology for simulating particle deposition on superhydrophobic surfaces with randomly distributed rough structures

Superhydrophobic coatings with a rough structure and low surface energy have been regarded as new high-efficiency anti-dust solutions. However, current research on the anti-dust mechanism of superhydrophobic materials is still insufficient. Main obstacles are the difficulty in determining the contact state between the dust particle and the rough structure and calculating the contact force accurately. Herein, a new methodology that introduces rough surface discretization and extends the Johnson-Kendall-Roberts (JKR) model to solve the adhesive contact forces of a particle-rough surface was proposed. Further, the lattice Boltzmann method coupled with the JKR-based discrete element method was employed to study the particle deposition characteristics on superhydrophobic surfaces with randomly distributed rough structures. Surfaces with different rough structures were reconstructed using the fast Fourier transformation method. The particle-airflow and rough surface-airflow interactions were calculated using the immersed moving boundary method. The coupling models and computation scheme were validated by comparing the simulation results with those recorded by a high-speed camera. The effects of the particle diameter, airflow inlet velocity, surface energy, and surface structures (characterized by skewness, kurtosis and standard deviation) on particle migration, deposition morphology and deposition rate were compared and analyzed. The results indicate that smaller particles with larger velocities are less likely to be deposited on superhydrophobic surface. Increasing the surface energy of the rough surface can significantly enhance the particle deposition rate owing to the strong particle–surface adhesion force. Further, appropriate surface roughness can reduce particle–surface adhesion and particle energy dissipation during collision, thereby leading to a deposition reduction. However, excessive peaks and deep valleys on the rough surface would hinder the rolling and translation of the particles, thereby resulting in particle accumulation on the surface. Finally, using superhydrophobic surfaces with a skewness of 0 and kurtosis of approximately 5.0, and appropriately reducing the standard deviation of the rough-surface structures can significantly enhance the effect of superhydrophobic surfaces on anti-dust performance.

Classification of Women's Voices Using Fast Fourier Transform (FFT) Method

Everyone has a different kind of voice. Based on gender, voice type is divided into six parts, namely soprano, mezzo soprano, and alto for women; and tenor, baritone, and bass in men. Each type of sound has a different range and with different frequencies. This study classified the type of voice in women using the Fast Fourier Transform (FFT) method by recording the voices of each user which would then be processed using the FFT method to obtain the appropriate sound range. This research got results with an accuracy of up to 80%.The results obtained from this study are quite appropriate and it is proven that the FFT method can be used in digital signal processing.

Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization.

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.

Linking a phase field model for polymer crystallization to full-field micromechanical simulations of semi-crystalline polymers

An enhanced phase field model recently proposed by the authors enables to model polymer crystallization and to predict spherulite growth and representative semi-crystalline micro-structures in 2D or 3D. After solidification, the value of the phase field variable in each subcell (2D pixel or 3D voxel) indicates whether the material is amorphous or crystalline. In the present paper, full-field micromechanical simulations are conducted on the microstructures generated by the enhanced phase field model in order to predict the effective mechanical properties. A Fast Fourier Transform (FFT) method with periodic boundary conditions is used. Care is taken to obtain representative volume elements (RVEs) by computing the number of subcells in each spherulite and the number of spherulite nucleations in each RVE. Numerical FFT predictions of elastic properties for a range of crystallinity ratios are validated against experimental data and compared to simpler composite inclusion models. Finite element simulations of thermal shrinkage are also shown. The approach was numerically implemented in a research version of the Digimat software.