Discrete Fourier Transform Analysis Research Articles

Smart fault detection of HTS coils using artificial intelligence techniques for large-scale superconducting electric transport applications

Superconducting coil is an essential and critical component in any superconducting apparatus used in large-scale power applications such as in superconducting machines of propulsion systems, in fault current limiters of the distribution system for future cryo-electric aircraft, or in windings of superconducting transformers for power grid applications. The superconducting coils in winding of large-scale power devices operate in kind of harsh environments from both temperature—considering liquid hydrogen or gaseous helium as coolant—(thermal stress) and electro-magneto-mechanical stress, point of views. Reliable operation of the coils in winding is of vital importance for the reliability of the superconducting device and the safety of the application that the device is used for. If the superconducting coil confronts a fault or an abnormal condition in the laboratory-level operation, it is straightforward to test the coil by measuring its critical current, AC loss, etc, to find whether it is damaged or not. However, there would be an urgent need to have faster and more intelligent fault detection and condition monitoring approaches with the possibility to become fully autonomous and real-time, in large-scale power applications, especially in sensitive applications such as in future cryo-electric aircraft, or in the fusion industry. To reach such intelligent fault-finding approaches, artificial intelligence-based techniques have been foreseen to be a promising solution. In this paper, we have developed an intelligent fault detection technique for finding a faulty superconducting coil, named the frequency-temporal classification method. This method has two main steps: first, this paper utilizes the discrete Fourier transform and independent component analysis to convert measurement signals of the healthy and faulty coils from (1) the time-series domain to the frequency domain; and (2) into time-series source signals. Second, this paper trains the support-vector machine using the derived frequency components. This trained model is then used for making fault detection for other superconducting coils. The developed technique can classify a fault with 99.2% accuracy.

An Improved Interpolated DFT-Based Parameter Identification for Sub-/Super-Synchronous Oscillations With Synchrophasors

The sub-synchronous resonance oscillations (SSO) caused by considerable renewable energy generations and power electronic devices seriously affect power system stability. Synchrophasor-based SSO parameter identification can effectively monitor SSOs. Aiming at the fundamental and frequency-coupled sub-/super-synchronous components that appear simultaneously in SSOs, an improved parameter identification algorithm is proposed in this paper based on the original interpolation Discrete Fourier Transform (DFT) and Hann-window. As the complement to the original algorithm, the improvements proposed in this paper additionally focus on the influence between the positive-/negative-frequency parts of the sub-/super-synchronous and fundamental components. Thus, the frequency, amplitude, and phase of the fundamental, sub-synchronous, and super-synchronous components can be accurately obtained by the proposed algorithm. This algorithm is compared to the original interpolated DFT and Prony analysis with both the simulated Phasor Measurement Unit (PMU) data and PMU data recorded during an actual SSO incident to verify its correctness and effectiveness. In the improved algorithm, the additional consideration for the super-synchronous component and the additional identified parameters significantly enhance the practical value of the interpolated DFT algorithm.

Atypical violence and conflict dynamics: evidence from Jerusalem

AbstractWhat is the impact of uncommon but notable violent acts on conflict dynamics? We analyze the impact of the murder of a Palestinian child on the broader dynamics of Israeli-Palestinian violence in Jerusalem. By using novel micro-level event data and utilizing Discrete Fourier Transform and Bayesian Poisson Change Point Analysis, we compare the impact of the murder to that of other lethal but more typical Israeli-Palestinian events. We demonstrate that the murder had a large and durable impact on the average number of daily riots in Jerusalem, whereas the other events caused smaller, short-term effects. We demonstrate that scholars should devote more attention to the analysis of atypical violent acts and indicate a set of tools for conducting such analyses.

Research on Analog-to-Digital Converter (ADC) Dynamic Parameter Method Based on the Sinusoidal Test Signal

The acquisition of dynamic analog-to-digital converter (ADC) parameters is becoming increasingly relevant as electronic information develops at a rapid pace. The fundamental challenge of the spectrum test for ADC is that coherent sampling is difficult to achieve, especially because of the high accuracy of the excitation signal necessary for coherent sampling. Therefore, incoherent sampling is certainly unavoidable in the actual test. If the coherent sampling condition is not reached in the test, spectrum leaks from test data after Discrete Fourier Transform (DFT) analysis, resulting in inaccurate parameters. In this study, a new breakdown and reconstruction method was presented using the Ensemble Empirical Mode Decomposition (EEMD); Hilbert transform and parameter fitting method can accurately estimate the incoherent fundamental and harmonic waves, then reconstruct them to obtain accurate ADC dynamic parameters.

Applications of the sampling uncertainty (SU)

The Sampling Uncertainty (SU) recently proposed allows the estimation of the sampling uncertainty including the Grouping and Segregation Error (GSE), the Fundamental Sampling Error (FSE) or the Fundamental Sampling Uncertainty (FSU) and the long range sampling errors for 1-dimensional sampling. SU is calculated from the spatial distribution of the analyte in a manner similar to cyclic convolution. For 1-dimensional sampling SU is shown to be better than variogram integration in case of cyclic or non-stationary variations and by being independent of the nugget effect when the nugget effect is close to zero. Three cases will be treated in detail: The problems with a low nugget effect, the effect of autocorrelation on 1-dimensional sampling and the effect of cyclic variations. For the effect of cyclic variations discrete Fourier transform (DFT) analysis is included to explain the findings and to filter the data. Finally, it is shown that DFT and SU give similar results for the cyclic pattern in the data when SU is calculated for a sampling ratio mass(lot)/mass(sample) = 2. Even though SU itself can estimate the sampling uncertainty correctly, it is proposed always to include a variographic analysis and a DFT analysis to characterize the spatial heterogeneity of the data.

Applications of the sampling uncertainty (SU)

The Sampling Uncertainty (SU) recently proposed allows the estimation of the sampling uncertainty including the Grouping and Segregation Error (GSE), the Fundamental Sampling Error (FSE) or the Fundamental Sampling Uncertainty (FSU) and the long range sampling errors for 1-dimensional sampling. SU is calculated from the spatial distribution of the analyte in a manner similar to cyclic convolution. For 1-dimensional sampling SU is shown to be better than variogram integration in case of cyclic or non-stationary variations and by being independent of the nugget effect when the nugget effect is close to zero. Three cases will be treated in detail: The problems with a low nugget effect, the effect of autocorrelation on 1-dimensional sampling and the effect of cyclic variations. For the effect of cyclic variations discrete Fourier transform (DFT) analysis is included to explain the findings and to filter the data. Finally, it is shown that DFT and SU give similar results for the cyclic pattern in the data when SU is calculated for a sampling ratio mass(lot)/mass(sample) = 2. Even though SU itself can estimate the sampling uncertainty correctly, it is proposed always to include a variographic analysis and a DFT analysis to characterize the spatial heterogeneity of the data.

Correlated multiple sampling technique - a discrete Fourier Transform analysis aimed for CMOS image sensors

Correlated multiple sampling technique - a discrete Fourier Transform analysis aimed for CMOS image sensors

Determination of Riboflavin by Nanocomposite Modified Carbon Paste Electrode in Biological Fluids Using Fast Fourier Transform Square Wave Voltammetry

Herein, fast Fourier transformation square-wave voltammetry (FFT-SWV) as a novel electrochemical determination technique was used to investigate the electrochemical behavior and determination of Riboflavin at the surface of a nanocomposite modified carbon paste electrode. The carbon paste electrode was modified by nanocomposite containing Samarium oxide (Sm2O3)/reduced graphene oxide (RGO) (2:1) to improve detection sensitivity of Riboflavin under optimal experimental conditions. Furthermore, the signal-to-noise ratio was significantly increased by application of discrete fast Fourier transformation analysis, background subtraction and two-dimensional integration of the electrode response over the selected potential range and the time window. Obtained cyclic voltammograms demonstrated a diffusion-controlled reversible electron transfer reaction for Riboflavin in phosphate buffer solution (pH=7.2). The peak potential values were pH-dependent, involving the same numbers of protons and electrons. To obtain the maximum sensitivity, some effective parameters such as scan rate (10 mV/s), accumulation time (0.2 s) and potential (+400 mV), frequency (1420 Hz) and amplitude (20 mV) were optimized. As a result, determination of Riboflavin using FFT-SWV showed a linear range of response from 10 to 400 nM (R2=0.9993), with limit of detection of 0.86 nM. An acceptable recovery percent was also obtained for Riboflavin in human plasma samples as criteria of measurement applicability of the proposed modified electrode.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper, we propose a hybrid forecasting model (HFM) for the short-term electric load forecasting using artificial neural network (ANN), discrete Fourier transformation (DFT) and principal component analysis (PCA) techniques in order to attain higher prediction accuracy. Firstly, we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation. Approximate curves, together with other input variables, are given to the ANN to predict the next-day hourly load curves. Furthermore, we predict PCA scores to obtain approximate load curves in the first step, which are then given to the ANN again in the second step. Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads. Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018, we train our models using historical data since January 2008. The forecast results show that the HFM consisting of “ANN with DFT” and “ANN with PCA” predicts next-day hourly loads more accurately than the conventional three-layered ANN approach. Their corresponding mean average absolute errors show 2.7% for ANN with DFT, 2.6% for ANN with PCA and 3.0% for the conventional ANN approach. We also find that in May, when electric demand is smaller with smaller fluctuations, forecasting errors are much smaller than January for all the models. Thus, we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

Remote Characterization of Dominant Wavelengths From Surface Folding on Lava Flows Using Lidar and Discrete Fourier Transform Analyses

AbstractSurface folding is common in lava flows of all compositions and is believed to be due to changes in viscosity and flow velocity between the cooling crust and the more fluid flow interior. However, our understanding of the relationship between surface folding and flow rheology is incomplete. In this study we analyze digital terrain models of eight lava flows ranging in composition from basaltic andesite to rhyolite using a discrete Fourier transform analysis to quantitatively determine dominant surface fold wavelengths. Our discrete Fourier transform analyses show that each lava flow has multiple fold generations and that dominant wavelengths are more closely related to calculated effective viscosity than to lava composition. At our Oregon sites, average dominant wavelengths generally increase with viscosity (r2=0.68), and the correlation improves (r2=0.87) when expanded by including previously measured fold wavelengths and viscosities from the global database. However, there are a few exceptions to this positive trend where a few lava flows have lower or higher than expected dominant fold wavelengths, which we infer are due to secondary factors such as differences in eruption conditions (eruption rate, temperature, etc.). Additionally, over a 5 order of magnitude range in viscosity, there is significant overlap between the ranges of fold wavelengths, particularly from 10 to 20m, for lavas from basaltic andesite to rhyolite, making it difficult to determine a numeric correlation between surface folds and lava rheology that would allow remote characterization of lava.

Light-induced birefringence in the isotropic phase of a lyotropic liquid crystal

In this paper, the light-induced order in the isotropic phase (I) of the KL-DeOH-water mixture is investigated. All mixtures show L1-I-L2 phase transitions being L1 and L2 two optically birefringent phases at low and high temperatures, respectively. The phase transitions were verified by polarized light microscopy technique. In the appropriate conditions of the relative concentration of the mixture and of temperature, an astonishing increase in the optical transmittance of the isotropic mixture can be observed in the presence of an external pulsed laser beam. The best induction effect was obtained with molar relative concentration C = [KL]/[DeOH] = 2.61 at about 2.0 °C from I-L2 phase transition. From each time-domain optical transmittance, the signal was analyzed in the low frequency domain (5 Hz < f < 50 Hz) by means of Discrete Fourier Transform (DFT). DFT analysis revealed two typical frequencies (ν1 and ν2) in the middle of the micellar isotropic phase with pronounced peaks in |F(ω)|. However, for mixtures with C = 2.61 (the lowest concentration), only one well pronounced peak at ν1 = 29.5Hz was found. For C = 2.62, the frequencies were ν1 = 29.5 Hz and ν2 = 36.5 Hz. And, for C = 2.63, ν1 = 36.5 Hz and ν2 = 40.0 Hz. It was verified that for all relative concentrations ν1 and ν2 have no dependence on temperature. These frequencies are being interpreted as an evidence of high correlation between the anisometric micelles (or correlation volumes) of the mixtures.

Assessing Lateral Interaction in the Synesthetic Visual Brain.

In grapheme-color synesthesia, letters and numbers evoke abnormal colored perceptions. Although the underlying mechanisms are not known, it is largely thought that the synesthetic brain is characterized by atypical connectivity throughout various brain regions, including the visual areas. To study the putative impact of synesthesia on the visual brain, we assessed lateral interactions (i.e., local functional connectivity between neighboring neurons in the visual cortex) by recording steady-state visual evoked potentials (ssVEPs) over the occipital region in color-grapheme synesthetes (n = 6) and controls (n = 21) using the windmill/dartboard paradigm. Discrete Fourier Transform analysis was conducted to extract the fundamental frequency and the second harmonics of ssVEP responses from contrast-reversing stimuli presented at 4.27 Hz. Lateral interactions were assessed using two amplitude-based indices: Short-range and long-range lateral interactions. Results indicated that synesthetes had a statistically weaker signal coherence of the fundamental frequency component compared to the controls, but no group differences were observed on lateral interaction indices. However, a significant correlation was found between long-range lateral interactions and the type of synesthesia experience (projector versus associator). We conclude that the occipital activity related to lateral interactions in synesthetes does not substantially differ from that observed in controls. Further investigation is needed to understand the impact of synesthesia on visual processing, specifically in relation to subjective experiences of synesthete individuals.

Diskretna Furijeova transformacija i cepstrum analiza pojave vibracija kod kamiona sa poluprikolicom

The aim of this paper is to present a novel technique to analyse the vibration signals during transportation in order to give new information to engineers to better understand these physical events. One of the primary sources of damages during transportation is the permanent vibration transmitted from the platform of semi-trailer to the cargo. The nature of this vibration depends on the road conditions and the features of the vehicle. In this study, a 2-axle-truck with a 3-axle-semi-trailer equipped with air spring suspension was observed to analyse the vibration circumstances. The affecting factors during the transportation were the type of the road (motorway, primary road, secondary road, tertiary road), and different vehicle speed levels. The vibration events were measured and registered on the platform of the trailer, then these values were evaluated in MATLAB environment. The presence of the harmonic vibrational components, the stationarity of the mode shapes, the characteristic frequency narrow-bands of the RMS and PSD values were primarily studied. The applied methods that were used are: discrete Fourier transformation, autocorrelation-, cross-correlation functions and cepstrum analysis.

Design and analysis of single loop and double loop photonic crystal ring resonator based on hexagonal lattice structure

Photonic Crystal (PC) based optical devices have been widely explored in recent era due to their capability of miniaturization and large-scale integration of optical and opto-electronic devices. In this paper, two dimensional PC based Single Loop, Double Loop (Horizontal) and Double Loop (Vertical) Ring Resonators using hexagonal lattice structure is designed and analyzed. It reports the Discrete Fourier Transform (DFT) analysis using Finite Difference Time Domain (FDTD) method and calculates the Photonic Band Gap (PBG) using Plane Wave Expansion (PWE) method. The comparative analysis of three configurations based on Q factor reports the outperformance of double loop photonic crystal ring resonator (PCRR) with lattice constants A = 30 and C = 30 at 1550 nm with Q factor 17,000. The Q factor for double loop horizontal and single loop structure is 16,300 and 15,500 at 1550 nm.

Heartbeat Signal Detection From Analysis of Airflow in Rat Airway Under Different Depths of Anaesthesia Conditions

We inserted a tubular flow sensor incorporating micro-electro-mechanical systems technologies directly into the rat airway and analyzed the airflow waveforms obtained under different depths of anaesthesia conditions by using discrete Fourier transformation (DFT) to evaluate the heartbeat frequency. The electrocardiogram (ECG) signal was used as the reference heartbeat frequency. The calibration curve of the flow sensor used to measure the oscillating airflow in the rat airway was determined on the basis of King’s law. The airflow waveform at the rat airway caused by only the heartbeat was measured by applying deep anaesthesia; the waveform frequency coincided with the simultaneously measured ECG signal frequency. DFT analysis of the airflow signals measured under deep and shallow anaesthesia conditions verified the fundamental heartbeat frequency values. The airflow components related to heartbeat motion were successfully extracted by using the heartbeat frequency spectrum. The respiration and heartbeat signals were thus successfully detected.

Time-frequency analyses of fluid–solid interaction under sinusoidal translational shear deformation of the viscoelastic rat cerebrum

During normal extracellular fluid (ECF) flow in the brain glymphatic system or during pathological flow induced by trauma resulting from impacts and blast waves, ECF–solid matter interactions result from sinusoidal shear waves in the brain and cranial arterial tissue, both heterogeneous biological tissues with high fluid content. The flow in the glymphatic system is known to be forced by pulsations of the cranial arteries at about 1 Hz. The experimental shear stress response to sinusoidal translational shear deformation at 1 Hz and 25% strain amplitude and either 0% or 33% compression is compared for rat cerebrum and bovine aortic tissue. Time-frequency analyses aim to correlate the shear stress signal frequency components over time with the behavior of brain tissue constituents to identify the physical source of the shear nonlinear viscoelastic response. Discrete fast Fourier transformation analysis and the novel application to the shear stress signal of harmonic wavelet decomposition both show significant 1 Hz and 3 Hz components. The 3 Hz component in brain tissue, whose magnitude is much larger than in aortic tissue, may result from interstitial fluid induced drag forces. The harmonic wavelet decomposition locates 3 Hz harmonics whose magnitudes decrease on subsequent cycles perhaps because of bond breaking that results in easier fluid movement. Both tissues exhibit transient shear stress softening similar to the Mullins effect in rubber. The form of a new mathematical model for the drag force produced by ECF–solid matter interactions captures the third harmonic seen experimentally.

Analysis of focused indirect ultrasound via high-speed spatially localized pressure sensing and its consequences on nucleation

Ultrasonication is frequently used to promote crystallization and chemical reactions, with many papers reporting the effects of ultrasonic parameters. High-speed pressure inducers (aka pinducers) and signal processing are used here to gain insights into the spatial localization of energy dissipation that occurs during ultrasonication, facilitating a design that allows ultrasonication to be spatially localized inside a tube, without requiring that the probe directly contact the solution. The fluid pressure measured inside of a flexible silicone tube pressed against an ultrasonic probe is observed to move between nearly constant amplitude oscillations to variable shape and amplitude waveforms that deviate significantly from being periodic. Ultrasonic power is transferred to the fluid inside the tube, generating a wide frequency range including via bubble oscillations. Discrete Fourier Transform analysis indicates complex interactions within the experimental system, in addition to the energy transferred inside the tubing at the expected ultrasonic source frequency. The total acoustic intensity decays exponentially with distance from the zone, dropping by more than two orders-of-magnitude for each 1cm increase in distance. The pinducer data analyses guide the design of an experimental setup in which crystals nucleate within fluid inside the zone in the tube, right under the ultrasonic probe.

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

Non-Harmonic Fourier Analysis for bladed wheels damage detection

The interaction between bladed wheels and the fluid distributed by the stator vanes results in cyclic loading of the rotating components. Compressors and turbines wheels are subject to vibration and fatigue issues, especially when resonance conditions are excited. Even if resonance conditions can be often predicted and avoided, high cycle fatigue failures can occur, causing safety issues and economic loss. Rigorous maintenance programs are then needed, forcing the system to expensive shut-down. Blade crack detection methods are beneficial for condition-based maintenance. While contact measurement systems are not always usable in exercise conditions (e.g. high temperature), non-contact methods can be more suitable. One (or more) stator-fixed sensor can measure all the blades as they pass by, in order to detect the damaged ones. The main drawback in this situation is the short acquisition time available for each blade, which is shortened by the high rotational speed of the components. A traditional Discrete Fourier Transform (DFT) analysis would result in a poor frequency resolution. A Non-Harmonic Fourier Analysis (NHFA) can be executed with an arbitrary frequency resolution instead, allowing to obtain frequency information even with short-time data samples. This paper shows an analytical investigation of the NHFA method. A data processing algorithm is then proposed to obtain frequency shift information from short time samples. The performances of this algorithm are then studied by experimental and numerical tests.

Measurement of absolute optical thickness of mask glass by wavelength-tuning Fourier analysis

Optical thickness is a fundamental characteristic of an optical component. A measurement method combining discrete Fourier-transform (DFT) analysis and a phase-shifting technique gives an appropriate value for the absolute optical thickness of a transparent plate. However, there is a systematic error caused by the nonlinearity of the phase-shifting technique. In this research the absolute optical-thickness distribution of mask blank glass was measured using DFT and wavelength-tuning Fizeau interferometry without using sensitive phase-shifting techniques. The error occurring during the DFT analysis was compensated for by using the unwrapping correlation. The experimental results indicated that the absolute optical thickness of mask glass was measured with an accuracy of 5nm.