Comment on Baumgart et al: Infrasound of a wind turbine reanalyzed as power spectrum and power spectral density (JSV, doi: 10.1016/j.jsv.2021.116310, 2021) – Comment on Pilger and Ceranna: The influence of periodic wind turbine noise on infrasound array measurements (JSV, Vol. 388, pp. 188–200, 2017)

2021 ◽  
pp. 116636
Author(s):  
Christoph Pilger ◽  
Lars Ceranna
Keyword(s):  
Power Spectrum ◽  
Spectral Density ◽  
Wind Turbine ◽  
Power Spectral Density ◽  
Wind Turbine Noise ◽  
Array Measurements ◽  
Power Spectral

An Intermittent Poisson Process Generating 1/f Noise with Possible Application to Fluorescence Intermittency

2014 ◽  
Vol 13 (02) ◽  
pp. 1450015 ◽  
Author(s):  
Ferdinand Grüneis
Keyword(s):  
Power Spectrum ◽  
Spectral Density ◽  
Poisson Process ◽  
Power Spectral Density ◽  
High Frequency ◽  
Frequency Region ◽  
Excess Noise ◽  
State Process ◽  
Power Spectral ◽  
Fluorescence Intermittency

Quantum dots (QD) and other nanoparticles exhibit fluorescence intermittency switching irregularly between bright ("on") and dark ("off") states. On- and off-times follow a power-law statistics with exponents ranging from -1 to -2. The empirical power spectral density of this two-state process shows a 1/fx shape with an exponent x reverting from ≈1 at low frequencies to ≈2 at high frequency. Based on theoretical considerations, the low frequency region can be attributed to the on-state; however, there are some discrepancies in attributing the off-states to the high frequency region. This difficulty can be overcome by introducing a Poisson process which is gated by the two-state process giving rise to an intermittent Poisson process (IPP); in this way, the statistical features of the two-state process are transferred to the IPP. The power spectral density of the IPP can be derived in closed form for arbitrarily distributed on/off-states. Besides shot noise the power spectrum of the IPP exhibits excess noise with two scaling regions which can be attributed to the respective on/off-states. The results are applied to interpret the power spectrum of fluorescence intermittency in QDs.

scholarly journals Spectral analysis for evaluation of the optical fiber cable performance using experimental and ANN techniques

2018 ◽  
Vol 7 (3) ◽  
pp. 1648 ◽  
Author(s):  
H. J. Abbas
Keyword(s):  
Power Spectrum ◽  
Optical Fiber ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Crack Depth ◽  
Maximum Error ◽  
Experimental Program ◽  
Phase Measurements ◽  
Power Spectral ◽  
Electrical Characterizations

This work deals with the performance evaluation of the optical fiber cables by calculating the changes in the power spectral density, power spectrum, and phase of the response signals from which the faults can be deducted and identified and accordingly the performance can be evaluated. An experimental program has been conducted by exciting the cable and then measuring the power spectral density, power spec-trum, and phase of the response signal for the faulty and un-faulty cables. The calculated and measured results are fed to the ANN system in order to train the ANN program, then the trained ANN package will be used as a parametric study of deduction and identification of cable faults. The ANN system detects and identities the faults in optical fiber. The experimental results power spectral density, power spectrum, and phase measurements are compared with ANN techniques results. Both results showed a good agreement with maximum error less than (1.26%). Finally, the paper shown with vibration test for cable and evaluate for electrical characterizations can be prediction for crack depth or location parameters. 

Rotational Turbulent Wind Field Simulation of Wind Turbine

2013 ◽  
Vol 291-294 ◽  
pp. 472-476 ◽  
Author(s):  
Wei He ◽  
De Tian ◽  
Qi Li ◽  
Ning Bo Wang
Keyword(s):  
Spectral Density ◽  
Wind Turbine ◽  
Power Spectral Density ◽  
Wind Velocity ◽  
Fourier Spectrum ◽  
Phase Lag ◽  
Turbulent Wind ◽  
Wind Field Simulation ◽  
Rotational Effect ◽  
Power Spectral

In order to accurately obtain the influence of rotational effect on fluctuating component of turbulent wind acted on wind turbine, rotational Fourier spectrum with considering rotational effect of rotor was deduced. Physical nature of the rotational Fourier spectrum embodied by coherence function and phase lag was indicated. Auto power spectral density and cross power spectral density of rotational Fourier spectrum with introducing phase lag were proposed. The spectrum matrix constructed by the module of rotational Fourier spectrum was decomposed with Cholesky's method, according to the spectrum representation method with introducing phase lag, the random turbulent wind speed field was generated by superposing a set of cosine functions. Finally, an example involving simulation of the longitudinal turbulent wind velocity time series of a 1.5 MW three-bladed pitch regulated wind turbine was investigated. The target spectrum and simulated spectrum were compared. The result shows that the proposed algorithm is more accurate to simulate the fluctuating wind velocity of rotational blade.

scholarly journals Study on Slurry Noise of Electromagnetic Flowmeter Based on ARMA Power Spectrum Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Song Gao ◽  
Bin Li
Keyword(s):  
Power Spectrum ◽  
Spectral Density ◽  
Coordinate System ◽  
Flow Velocity ◽  
Power Spectral Density ◽  
Electromagnetic Flowmeter ◽  
Noise Power ◽  
Noise Power Spectral Density ◽  
Power Spectral ◽  
Power Spectral Density Curve

The output signal of the electromagnetic flowmeter measuring the slurry flow will be disturbed by the slurry noise. It is necessary to study the characteristics of slurry noise in order to reduce the interference. The methodology for the estimation of the slurry noise power spectrum based on the ARMA model is presented in this paper, and the relation among the slurry noise, the velocity, and the concentration is obtained by means of the methodology above according to the1/fcharacteristics of the slurry noise. The results by computer simulation experiment show that if the concentration or flow velocity is increased, then slurry noise power spectral density curve in the logarithmic coordinate system will move to the upper right; if the concentration or flow velocity is reduced, then slurry noise power spectral density curve in the logarithmic coordinate system will move to the lower left.

Enhanced Power Spectrum Estimates Using Kalman Reconstruction

1998 ◽  
Vol 120 (2) ◽  
pp. 253-256 ◽  
Author(s):  
Lance H. Benedict ◽  
Richard D. Gould
Keyword(s):  
Power Spectrum ◽  
Spectral Density ◽  
Power Spectral Density ◽  
High Frequency ◽  
Laser Doppler Anemometer ◽  
Transform Method ◽  
Raw Data ◽  
Frequency Information ◽  
Power Spectral ◽  
Time Histories

A number of power spectral density (PSD) estimators were assessed using real laser Doppler anemometer (LDA) data from grid generated turbulence. PSD estimates from the raw data via the slotting technique and direct transform method were compared to those estimated from sample and hold, linear, and Kalman reconstructed velocity time histories. Of the reconstruction schemes, only Kalman reconstruction was shown to significantly reduce the effects of noise on the measurements leading to an additional 2 decades in power of high frequency information.

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