scholarly journals Symbolic time series characterization and block entropy analysis of DC-DC converters

2008 ◽  
Vol 57 (10) ◽  
pp. 6112
Author(s):  
Wang Xue-Mei ◽  
Zhang Bo ◽  
Qiu Dong-Yuan ◽  
Chen Liang-Gang
Keyword(s):  
Time Series ◽  
Entropy Analysis ◽  
Block Entropy

scholarly journals Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 307
Author(s):  
Dimitrios Nikolopoulos ◽  
Aftab Alam ◽  
Ermioni Petraki ◽  
Michail Papoutsidakis ◽  
Panayiotis Yannakopoulos ◽  
...  
Keyword(s):  
Time Series ◽  
Symbolic Dynamics ◽  
Long Memory ◽  
Tsallis Entropy ◽  
Entropy Analysis ◽  
Boltzmann Entropy ◽  
Analysis Techniques ◽  
Block Entropy ◽  
Self Organisation ◽  
First Time

This paper utilises statistical and entropy methods for the investigation of a 17-year PM10 time series recorded from five stations in Athens, Greece, in order to delineate existing stochastic and self-organisation trends. Stochastic patterns are analysed via lumping and sliding, in windows of various lengths. Decreasing trends are found between Windows 1 and 3500–4000, for all stations. Self-organisation is studied through Boltzmann and Tsallis entropy via sliding and symbolic dynamics in selected parts. Several values are below −2 (Boltzmann entropy) and 1.18 (Tsallis entropy) over the Boltzmann constant. A published method is utilised to locate areas for which the PM10 system is out of stochastic behaviour and, simultaneously, exhibits critical self-organised tendencies. Sixty-six two-month windows are found for various dates. From these, nine are common to at least three different stations. Combining previous publications, two areas are non-stochastic and exhibit, simultaneously, fractal, long-memory and self-organisation patterns through a combination of 15 different fractal and SOC analysis techniques. In these areas, block-entropy (range 0.650–2.924) is significantly lower compared to the remaining areas of non-stochastic but self-organisation trends. It is the first time to utilise entropy analysis for PM10 series and, importantly, in combination with results from previously published fractal methods.

scholarly journals Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 245
Author(s):  
Ildoo Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Soap Film ◽  
Sample Entropy ◽  
Series Data ◽  
Entropy Analysis ◽  
Measured Time ◽  
Energetic Analysis ◽  
Turbulence Data ◽  
Decaying Turbulence

Multiscale sample entropy analysis has been developed to quantify the complexity and the predictability of a time series, originally developed for physiological time series. In this study, the analysis was applied to the turbulence data. We measured time series data for the velocity fluctuation, in either the longitudinal or transverse direction, of turbulent soap film flows at various locations. The research was to assess the feasibility of using the entropy analysis to qualitatively characterize turbulence, without using any conventional energetic analysis of turbulence. The study showed that the application of the entropy analysis to the turbulence data is promising. From the analysis, we successfully captured two important features of the turbulent soap films. It is indicated that the turbulence is anisotropic from the directional disparity. In addition, we observed that the most unpredictable time scale increases with the downstream distance, which is an indication of the decaying turbulence.

scholarly journals Identifying global patterns of stochasticity and nonlinearity in the Earth System

2016 ◽  
Author(s):  
Fernando Arizmendi ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Earth System ◽  
Entropy Analysis ◽  
Solar Forcing ◽  
The Earth ◽  
Geographical Regions ◽  
Global Patterns

Abstract. By comparing time-series of surface air temperature (SAT, monthly reanalysis data from NCEP CDAS1 and ERA Interim) with respect to the top-of-atmosphere incoming solar radiation (the insolation), we perform a detailed analysis of the SAT response to solar forcing. By computing the entropy of SAT time-series, we also quantify the degree of stochasticity. We find spatial coherent structures which are characterized by high stochasticity and nearly linear response to solar forcing (the shape of SAT time-series closely follows that of the isolation), or vice versa. The entropy analysis also allows to identify geographical regions in which there are significant differences between the NCEP CDAS1 and ERA Interim datasets, which are due to the presence of extreme values in one dataset but not in the other. Therefore, entropy maps are a valuable tool for anomaly detection and model inter-comparisons.

scholarly journals Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 798
Author(s):  
José Javier Reyes-Lagos ◽  
Adriana Cristina Pliego-Carrillo ◽  
Claudia Ivette Ledesma-Ramírez ◽  
Miguel Ángel Peña-Castillo ◽  
María Teresa García-González ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Coarse Graining ◽  
Entropy Analysis ◽  
Third Trimester ◽  
K Value ◽  
Order Difference ◽  
Robust Approach ◽  
The Third ◽  
Nonlinear Features

Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition.

scholarly journals Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Climate ◽  
2015 ◽  
Vol 3 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Heiko Balzter ◽  
Nicholas Tate ◽  
Jörg Kaduk ◽  
David Harper ◽  
Susan Page ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Entropy Analysis ◽  
Climate Data ◽  
Multi Scale ◽  
Series Analysis

scholarly journals Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e102833 ◽  
Author(s):  
Patricia Wollstadt ◽  
Mario Martínez-Zarzuela ◽  
Raul Vicente ◽  
Francisco J. Díaz-Pernas ◽  
Michael Wibral
Keyword(s):  
Time Series ◽  
Transfer Entropy ◽  
Entropy Analysis ◽  
Efficient Transfer

BLOCK ENTROPY ANALYSIS OF HEART RATE VARIABILITY SIGNALS

2006 ◽  
Vol 16 (07) ◽  
pp. 2093-2101 ◽  
Author(s):  
K. KARAMANOS ◽  
S. NIKOLOPOULOS ◽  
K. HIZANIDIS ◽  
G. MANIS ◽  
A. ALEXANDRIDI ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Heat Rate ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Entropy Analysis ◽  
Data Set ◽  
Novel Approach ◽  
Data Files ◽  
Block Entropy ◽  
Artery Disease

In this paper we present a novel approach to the analysis of Heat Rate Variability (HRV) data, by coarse-graining analysis using the estimation of Block Entropies with the technique of lumping. HRV time series are generated from long recordings of Electrocardiograms (ECGs) and are then filtered in order to produce a coarse-grained symbolic dynamics. Block Entropy analysis is applied to these dynamics in order to examine its coarse-grained statistics. Our data set is comprised of two subsets, one of healthy subjects and another of Coronary Artery Disease (CAD) patients. It is found that Entropy analysis provides a quick and efficient tool for the differentiation of these series according to subject category. Healthy subjects provided more complex statistics compared to patients; specifically, the healthy data files provided higher values of block Entropies compared to patient ones. We also compare these results with the Correlation Dimension Estimation in order to establish coherency. We believe that this analysis may provide a useful statistical method towards the better understanding of the human cardiac system.

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