scholarly journals Electrochemical Noise Chaotic Analysis of NiCoAg Alloy in Hank Solution

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
D. Bahena ◽  
I. Rosales ◽  
O. Sarmiento ◽  
R. Guardián ◽  
C. Menchaca ◽  
...  
Keyword(s):  
Time Series ◽  
Electrochemical Impedance ◽  
Random Noise ◽  
Deterministic Chaos ◽  
Fractal Dimensions ◽  
Corrosion Mechanism ◽  
Power Spectral ◽  
Fractal Analyses ◽  
Power Spectral Densities ◽  
Hank Solution

The potential and current oscillations during corrosion of NiCoAg alloy in Hank solution were studied. Detailed nonlinear fractal analyses were used to characterize complex time series clearly showing that the irregularity in these time series corresponds to deterministic chaos rather than to random noise. The chaotic oscillations were characterized by power spectral densities, phase space, and Lyapunov exponents. Electrochemical impedance was also applied the fractal dimensions for the corroded surface was obtained, and a corrosion mechanism was proposed.

scholarly journals Time-series Imputation Algorithm

2021 ◽  
Author(s):  
David Howe
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Time Series Data ◽  
Series Data ◽  
Allan Deviation ◽  
Original Time Series ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
The Fourier Transform ◽  
Original Time

Statistical imputation is a field of study that attempts to fill missing data. It is commonly applied to population statistics whose data have no correlation with running time. For a time series, data is typically analyzed using the autocorrelation function (ACF), the Fourier transform to estimate power spectral densities (PSD), the Allan deviation (ADEV), trend extensions, and basically any analysis that depends on uniform time indexes. We explain the rationale for an imputation algorithm that fills gaps in a time series by applying a backward, inverted replica of adjacent live data. To illustrate, four intentional massive gaps that exceed 100% of the original time series are recovered. The L(f) PSD with imputation applied to the gaps is nearly indistinguishable from the original. Also, the confidence of ADEV with imputation falls within 90% of the original ADEV with mixtures of power-law noises. The algorithm in Python is included for those wishing to try it.

TIME SERIES ANALYSIS OF COMPLEX DYNAMICAL BEHAVIOR CONTAMINATED WITH OBSERVATIONAL NOISE

1996 ◽  
Vol 06 (11) ◽  
pp. 2031-2045 ◽  
Author(s):  
TAKAYA MIYANO
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Random Noise ◽  
Dynamical Behavior ◽  
Deterministic Chaos ◽  
Diagnostic Algorithm ◽  
Diagnostic Methods ◽  
Observational Noise ◽  
Nonlinear Forecasting ◽  
Complex Dynamical Behavior

Diagnostic methods for discovering deterministic chaos based on the instability and the parallelness of nearby trajectories generated from a time series in phase space are applied to numerical time series contaminated with additive random noise. The diagnostic algorithm based on nonlinear forecasting is prone to be fooled when handling chaotic data including observational noise. Such a misdiagnosis can be circumvented by estimating the degrees of parallelness of neighboring trajectories in the phase space. Dynamical properties of global temperature variations and voice signals of Japanese vowel /a/ are examined by the combinational use of the diagnostic algorithms.

scholarly journals The Application of Random Noise Reduction By Nearest Neighbor Method To Forecasting of Economic Time Series

2014 ◽  
Vol 13 (2) ◽  
pp. 96-108
Author(s):  
Monika Miśkiewicz-Nawrocka
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Financial Time Series ◽  
Random Noise ◽  
Time Series Prediction ◽  
Deterministic Chaos ◽  
Largest Lyapunov Exponent ◽  
Economic Time Series ◽  
Economic Time

Abstract Since the deterministic chaos appeared in the literature, we have observed a huge increase in interest in nonlinear dynamic systems theory among researchers, which has led to the creation of new methods of time series prediction, e.g. the largest Lyapunov exponent method and the nearest neighbor method. Real time series are usually disturbed by random noise, which can complicate the problem of forecasting of time series. Since the presence of noise in the data can significantly affect the quality of forecasts, the aim of the paper will be to evaluate the accuracy of predicting the time series filtered using the nearest neighbor method. The test will be conducted on the basis of selected financial time series.

Determinism Measurement in Time Series by Chaotic Approach and Its Applications

1999 ◽  
Vol 3 (1) ◽  
pp. 50-55 ◽  
Author(s):  
Yasunari Fujimoto ◽  
◽  
Tadashi Iokibe
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Random Noise ◽  
Automatic Transmission ◽  
Deterministic Chaos ◽  
Chaotic Time Series ◽  
Practical Application ◽  
Characteristic Frequencies ◽  
Parallel Measurement ◽  
Continuous Power

Frequently irregular-looking time series may have a deterministic cause, which is why it is called deterministic chaos. Even if a time series has a little noise, it is not always easy to recognize noise by looking at data. Fast Fourier transform (FFT) is used to extract characteristic frequencies. A chaotic time series consists of an infinite number of frequency elements, producing a broad continuous power spectrum but has few distinguishable characteristics. We propose a way, called trajectory parallel measurement (TPM), based on the chaotic approach to distinguish determinism and randomness in a time series, and apply this to a chaotic time series with random noise, summarizing the results of practical application in diagnosing automatic transmission.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

Effect of CO2 on Anti-Corrosion Property of a Polyurea Coating Modified with Organosilicone

2014 ◽  
Vol 789 ◽  
pp. 466-470
Author(s):  
Qing Hao Shi ◽  
Bing Ying Wang ◽  
Bin Zhao
Keyword(s):  
High Temperature ◽  
Partial Pressure ◽  
Composite Coating ◽  
Corrosion Test ◽  
Electrochemical Impedance ◽  
Failure Process ◽  
Corrosion Property ◽  
Corrosion Mechanism ◽  
Partial Pressures ◽  
High Temperature High Pressure

The corrosion mechanism of organic silicon modified polyurea composite coating under different CO2 partial pressures was studied using high-temperature autoclave, combined with scanning electron microscopy (SEM), adhesion tests and electrochemical impedance spectroscopy (EIS) technology. The experimental results showed that: there was no corrosion product formed on the surface of coating sample after high-temperature high-pressure corrosion test, and with the increasing of CO2 partial pressure, the coating adhesion and impedance values decline increases. Moreover CO2 partial pressure increases accelerated the failure process of polyurea composite coating system.

Analysis of power spectral densities of electrocardiograms

1971 ◽  
Vol 12 (1-2) ◽  
pp. 41-51 ◽  
Author(s):  
Vrudhula K. Murthy ◽  
L. Julian Haywood ◽  
John Richardson ◽  
Robert Kalaba ◽  
Steven Salzberg ◽  
...  
Keyword(s):  
Spectral Densities ◽  
Power Spectral ◽  
Power Spectral Densities
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