scholarly journals Bilinear Time Series in Signal Analysis

10.5772/5391 ◽  
2008 ◽  
Author(s):  
Bielinska Ewa
Keyword(s):  
Time Series ◽  
Signal Analysis

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

scholarly journals F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
R. M. Dünki ◽  
M. Dressel
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Signal Analysis ◽  
Feature Vector ◽  
Spectral Power ◽  
Ratio Test ◽  
Test Statistics ◽  
Biomedical Signal ◽  
Statistical Assessment ◽  
Biomedical Signal Analysis

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ2(F)/F2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

scholarly journals ANALISIS MULTIRESOLUSI WAVELET DENGAN TRANSFORMASI WAVELET DISKRIT BERBASIS GUI R (STUDI KASUS: INFLASI DI INDONESIA PADA PERIODE OKTOBER 2007-MEI 2018)

2020 ◽  
Vol 9 (2) ◽  
pp. 143-151
Author(s):  
Sania Anisa Farah ◽  
Suparti Suparti ◽  
Dwi Ispriyanti
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Graphical User Interface ◽  
Signal Analysis ◽  
Parametric Method ◽  
Analysis Data ◽  
Statistical Computing ◽  
Discrete Wavelet ◽  
Command Line ◽  
Command Line Interface

Lately, the wavelet applications are widely used in statistics, one of them is discrete wavelet transform (DWT) which is a non-parametric method for signal analysis, data compression, and time series analysis. As technology becomes more advanced, a software is necessary to support the statistical analysis by such method, one of them being the open source based R. It is often used in statistical computing with command line interface (CLI) which requires the R user to remember the names of syntaxes and functions. It becomes less effective when there are many related statistical analysis involved, so graphical user interface (GUI) is needed to access all of them easily. The testing of multiresolution analysis by DWT for Haar, Daublets, and Coiflets filters with levels 1-6 had been performed by using the inflation data in Indonesia during October 2007-May 2018 taken from Bank Indonesia website. The result shows that the sixth level of DWT gives the best estimation for each filters, and Daublets 20 is the best filter for overall estimation with MSE, MAPE, and MASE values are 0.05755, 3.40678, and 0.35343 respectively. The packages for GUI construction in R are wavelets and shiny. Based on its usage, the GUI is capable of processing the chosen analysis and showing the valid output.

scholarly journals Short-time fractal analysis of biological autoluminescence

2019 ◽  
Author(s):  
Martin Dlask ◽  
Jaromír Kukal ◽  
Michaela Poplová ◽  
Pavel Sovka ◽  
Michal Cifra
Keyword(s):  
Time Series ◽  
Experimental Work ◽  
Fractal Analysis ◽  
Signal Analysis ◽  
Brownian Bridge ◽  
Physiological Processes ◽  
Reference Signals ◽  
Negative Memory ◽  
Short Time ◽  
External Stimuli

AbstractBiological systems manifest continuous weak autoluminescence, which is present even in the absence of external stimuli. Since this autoluminescence arises from internal metabolic and physiological processes, several works suggested that it could carry information in the time series of the detected photon counts. However, there is little experimental work which would show any difference of this signal from random Poisson noise and some works were prone to artifacts due to lacking or improper reference signals. Here we apply rigorous statistical methods and advanced reference signals to test the hypothesis whether time series of autoluminescence from germinating mung beans display any intrinsic correlations. Utilizing the fractional Brownian bridge that employs short samples of time-series in the method kernel, we suggest that the detected autoluminescence signal from mung beans is not totally random, but it seems to involve a process with a negative memory. Our results contribute to the development of the rigorous methodology of signal analysis of photonic biosignals.

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