scholarly journals Evidence for nonlinear and chaotic behaviour in pulsar spin-down rates

2012 ◽  
Vol 8 (S291) ◽  
pp. 495-495
Author(s):  
Andrew Seymour ◽  
Duncan Lorimer
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Nonlinear Equations ◽  
Strange Attractor ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Structural Information ◽  
Chaotic Behaviour ◽  
Present Evidence

AbstractWe present evidence for chaotic dynamics in pulsar spin-down rates originally measured by Lyne et al. (2010). Using techniques that allow us to re-sample the original measurements without losing structural information, we have searched for evidence for a strange attractor in the time series of frequency derivative for each pulsar. Our measurements of correlation dimension and Lyapunov exponent show, particularly in the case of PSR B1828-11, that the underlying behavior appears to be driven by a strange attractor with approximately three governing nonlinear equations.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

Gear Damage Detection Using Chaotic Dynamics Techniques: A Preliminary Study

1995 ◽  
Author(s):  
M. Farid Golnaraghi ◽  
DerChyan Lin ◽  
Paul Fromme
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Crack Detection ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Nonlinear Time Series Analysis ◽  
Preliminary Study

Abstract This paper is a preliminary study applying nonlinear time series analysis to crack detection in gearboxes. Our investigations show that the vibration signal emerging from a gearbox is chaotic. Appearance of a crack in a gear tooth alters this response and hence the chaotic signature. We used correlation dimension and Lyapunov exponents to quantify this change. The main goal of this study is to point out the great potential of these methods in detection of cracks and faults in machinery.

scholarly journals The transient variation in the complexes of the low-latitude ionosphere within the equatorial ionization anomaly region of Nigeria

2015 ◽  
Vol 22 (5) ◽  
pp. 527-543 ◽  
Author(s):  
A. B. Rabiu ◽  
B. O. Ogunsua ◽  
I. A. Fuwape ◽  
J. A. Laoye
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Tsallis Entropy ◽  
Variation Pattern ◽  
Dynamical Complexity ◽  
Equatorial Ionization Anomaly ◽  
Non Linear ◽  
Anomaly Region

Abstract. The quest to find an index for proper characterization and description of the dynamical response of the ionosphere to external influences and its various internal irregularities has led to the study of the day-to-day variations of the chaoticity and dynamical complexity of the ionosphere. This study was conducted using Global Positioning System (GPS) total electron content (TEC) time series, measured in the year 2011, from five GPS receiver stations in Nigeria, which lies within the equatorial ionization anomaly region. The non-linear aspects of the TEC time series were obtained by detrending the data. The detrended TEC time series were subjected to various analyses to obtain the phase space reconstruction and to compute the chaotic quantifiers, which are Lyapunov exponents LE, correlation dimension, and Tsallis entropy, for the study of dynamical complexity. Considering all the days of the year, the daily/transient variations show no definite pattern for each month, but day-to-day values of Lyapunov exponents for the entire year show a wavelike semiannual variation pattern with lower values around March, April, September and October. This can be seen from the correlation dimension with values between 2.7 and 3.2, with lower values occurring mostly during storm periods, demonstrating a phase transition from higher dimension during the quiet periods to lower dimension during storms for most of the stations. The values of Tsallis entropy show a similar variation pattern to that of the Lyapunov exponent, with both quantifiers correlating within the range of 0.79 to 0.82. These results show that both quantifiers can be further used together as indices in the study of the variations of the dynamical complexity of the ionosphere. The presence of chaos and high variations in the dynamical complexity, even in quiet periods in the ionosphere, may be due to the internal dynamics and inherent irregularities of the ionosphere which exhibit non-linear properties. However, this inherent dynamics may be complicated by external factors like geomagnetic storms. This may be the main reason for the drop in the values of the Lyapunov exponent and Tsallis entropy during storms. The dynamical behaviour of the ionosphere throughout the year, as described by these quantifiers, was discussed in this work.

scholarly journals A comparative study on chaoticity of equatorial/low latitude ionosphere over Indian subcontinent during geomagnetically quiet and disturbed periods

2010 ◽  
Vol 17 (6) ◽  
pp. 765-776 ◽  
Author(s):  
K. Unnikrishnan
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Correlation Dimension ◽  
Linear Prediction ◽  
Indian Subcontinent ◽  
Surrogate Data ◽  
Chaotic Behaviour ◽  
Quiet Period ◽  
Nonlinear Prediction ◽  
Non Linear

Abstract. In the present study, the latitudinal aspect of chaotic behaviour of ionosphere during quiet and storm periods are analyzed and compared by using GPS TEC time series measured at equatorial trough, crest and outside crest stations over Indian subcontinent, by employing the chaotic quantifiers like Lyapunov exponent (LE), correlation dimension (CD), entropy and nonlinear prediction error (NPE). It is observed that the values of LE are low for storm periods compared to those of quiet periods for all the stations considered here. The lowest value of LE is observed at the trough station, Agatti (2.38° N, Geomagnetically), and highest at crest station, Mumbai (10.09° N, Geomagnetically) for both quiet and storm periods. The values of correlation dimension computed for TEC time series are in the range 2.23–2.74 for quiet period, which indicate that equatorial ionosphere may be described with three variables during quiet period. But the crest station Mumbai shows a higher value of CD (3.373) during storm time, which asserts that four variables are necessary to describe the system during storm period. The values of non linear prediction error (NPE) are lower for Agatti (2.38° N, Geomagnetically) and Jodhpur (18.3° N, Geomagnetically), during storm period, compared to those of quiet period, mainly because of the predominance of non linear aspects during storm periods The surrogate data test is carried out and on the basis of the significance of difference of the original data and surrogates for various aspects, the surrogate data test rejects the null hypothesis that the time series of TEC during storm and quiet times represent a linear stochastic process. It is also observed that using state space model, detrended TEC can be predicted, which reasonably reproduces the observed data. Based on the values of the above quantifiers, the features of chaotic behaviour of equatorial trough crest and outside the crest regions of ionosphere during geomagnetically quiet and disturbed periods are briefly discussed.

OPTIMAL RECONSTRUCTION SPACE FOR ESTIMATING CORRELATION DIMENSION

1996 ◽  
Vol 06 (02) ◽  
pp. 377-381 ◽  
Author(s):  
ROBERT C. HILBORN ◽  
MINGZHOU DING
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Correlation Dimension ◽  
Chaotic Time Series ◽  
Largest Lyapunov Exponent ◽  
Correlation Integral ◽  
Past Work ◽  
Optimal Reconstruction ◽  
Scaling Region ◽  
Delay Coordinates

In this paper we consider the estimation of the correlation dimension from a scalar chaotic time series using delay coordinates. Past work has shown that there appears to be a reconstruction space for which the correlation integral has the longest scaling region. We give a firmer foundation to this idea by developing a theory that estimates the dimension of this “optimal” reconstruction space in terms of dynamical quantities such as the largest Lyapunov exponent.

scholarly journals On the Dimension of the Solar Activity Attractor

1993 ◽  
Vol 157 ◽  
pp. 91-95
Author(s):  
C.M. Orzaru
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Solar Activity ◽  
Correlation Dimension ◽  
Chaotic Behaviour ◽  
The Other ◽  
Short Series ◽  
Sunspot Numbers ◽  
First Case ◽  
Characteristic Measure

The correlation dimension D(2) as a characteristic measure of the regular or chaotic behaviour of the solar dynamical system has been calculated. The algorithm suggested by Grassberger and Procaccia (1983) has been applied to time series of relative sunspot numbers and of areas of sunspots and faculae. In the first case, a correlation dimension D(2) ≃ 1.5 has been found; in the other two cases, the algorithm was not convergent, the results obtained being not relevant, due to the too short series of data available.

FRACTAL AND CHAOS ANALYSIS FOR DYNAMICS OF RADON EXHALATION FROM URANIUM MILL TAILINGS

Fractals ◽  
2016 ◽  
Vol 24 (03) ◽  
pp. 1650029 ◽  
Author(s):  
YONGMEI LI ◽  
WANYU TAN ◽  
KAIXUAN TAN ◽  
ZEHUA LIU ◽  
YANSHI XIE
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Hurst Exponent ◽  
Correlation Dimension ◽  
Radon Exhalation Rate ◽  
Exhalation Rate ◽  
Radon Exhalation ◽  
Mill Tailings ◽  
Uranium Tailings ◽  
Uranium Mill Tailings

Tailings from mining and milling of uranium ores potentially are large volumes of low-level radioactive materials. A typical environmental problem associated with uranium tailings is radon exhalation, which can significantly pose risks to environment and human health. In order to reduce these risks, it is essential to study the dynamical nature and underlying mechanism of radon exhalation from uranium mill tailings. This motivates the conduction of this study, which is based on the fractal and chaotic methods (e.g. calculating the Hurst exponent, Lyapunov exponent and correlation dimension) and laboratory experiments of the radon exhalation rates. The experimental results show that the radon exhalation rate from uranium mill tailings is highly oscillated. In addition, the nonlinear analyses of the time series of radon exhalation rate demonstrate the following points: (1) the value of Hurst exponent much larger than 0.5 indicates non-random behavior of the radon time series; (2) the positive Lyapunov exponent and non-integer correlation dimension of the time series imply that the radon exhalation from uranium tailings is a chaotic dynamical process; (3) the required minimum number of variables should be five to describe the time evolution of radon exhalation. Therefore, it can be concluded that the internal factors, including heterogeneous distribution of radium, and randomness of radium decay, as well as the fractal characteristics of the tailings, can result in the chaotic evolution of radon exhalation from the tailings.

Analysis and Identification of Chaotic Dynamics in a Flying Vibratory System

2004 ◽  
Author(s):  
Jin-Wei Liang ◽  
Shy-Leh Chen ◽  
Ching-Ming Yen
Keyword(s):  
Time Series ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Chaotic Behavior ◽  
Random Noise ◽  
Maximum Lyapunov Exponent ◽  
Embedding Dimension ◽  
Vibratory System ◽  
Experimental Time Series ◽  
Acceleration Signals

This paper aims at determining whether chaotic dynamics exist in a flying vibratory system. It is important to identify chaotic behavior in a flying system since it may jeopardize the structure of the flying object and cause instability subsequently. It can also cause uncomfortable experience for passengers in a passenger airplane or inaccurate targeting for a missile. Identification of chaotic dynamics from experimental time series is a nontrivial task, since the data is likely to be contaminated with random noise that possesses similar properties to chaos. In this work, acceleration signals were measured at nine different locations or orientations of the flying object during a test fly. Steady-state acceleration signals were extracted and analyzed. The analysis is based on the pseudo phase-space trajectories reconstructed from the experimental time series using the method of delays. Two indices, the correlation dimension and the maximum Lyapunov exponent, are employed to identify the chaotic behavior and to distinguish it from random noise. In general, the correlation dimension calculated from the pseudo trajectory depends on the embedding dimension. It is found in three of the nine-channel signals that the correlation dimension saturates when the embedding dimension is larger than a critical value. The critical embedding dimension is the minimum dimension required for fully un-stretching the phase-space trajectories. This phenomenon indicates a possible existence of chaotic dynamics. It is also found that the maximum Lyapunov exponents calculated from the same acceleration signals are all positive, which further verifies the possibility of the existence of chaotic motion. In addition, some computational issues regarding the embedding dimension, correlation dimension, and maximum Lyapunov exponent are discussed in this paper.

scholarly journals Chaotic dynamics of cardioventilatory coupling in humans: effects of ventilatory modes

2009 ◽  
Vol 296 (4) ◽  
pp. R1088-R1097 ◽  
Author(s):  
Laurence Mangin ◽  
Christine Clerici ◽  
Thomas Similowski ◽  
Chi-Sang Poon
Keyword(s):  
Time Series ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Chaotic Behavior ◽  
Intrathoracic Pressure ◽  
Ventilator Weaning ◽  
Largest Lyapunov Exponent ◽  
Ventilatory Mode ◽  
Limit Value ◽  
Noise Limit

Cardioventilatory coupling (CVC), a transient temporal alignment between the heartbeat and inspiratory activity, has been studied in animals and humans mainly during anesthesia. The origin of the coupling remains uncertain, whether or not ventilation is a main determinant in the CVC process and whether the coupling exhibits chaotic behavior. In this frame, we studied sedative-free, mechanically ventilated patients experiencing rapid sequential changes in breathing control during ventilator weaning during a switch from a machine-controlled assistance mode [assist-controlled ventilation (ACV)] to a patient-driven mode [inspiratory pressure support (IPS) and unsupported spontaneous breathing (USB)]. Time series were computed as R to start inspiration (RI) and R to the start of expiration (RE). Chaos was characterized with the noise titration method (noise limit), largest Lyapunov exponent (LLE) and correlation dimension (CD). All the RI and RE time series exhibit chaotic behavior. Specific coupling patterns were displayed in each ventilatory mode, and these patterns exhibited different linear and chaotic dynamics. When switching from ACV to IPS, partial inspiratory loading decreases the noise limit value, the LLE, and the correlation dimension of the RI and RE time series in parallel, whereas decreasing intrathoracic pressure from IPS to USB has the opposite effect. Coupling with expiration exhibits higher complexity than coupling with inspiration during mechanical ventilation either during ACV or IPS, probably due to active expiration. Only 33% of the cardiac time series (RR interval) exhibit complexity either during ACV, IPS, or USB making the contribution of the cardiac signal to the chaotic feature of the coupling minimal. We conclude that 1) CVC in unsedated humans exhibits a complex dynamic that can be chaotic, and 2) ventilatory mode has major effects on the linear and chaotic features of the coupling. Taken together these findings reinforce the role of ventilation in the CVC process.

scholarly journals Chaotic Dynamics of Flags from Recurring Values of Flapping Moment

2017 ◽  
Vol 27 (02) ◽  
pp. 1750020 ◽  
Author(s):  
Emmanuel Virot ◽  
Davide Faranda ◽  
Xavier Amandolese ◽  
Pascal Hémon
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Dynamics ◽  
Point Of View ◽  
Experimental Point ◽  
Maximal Lyapunov Exponent ◽  
Limited Size ◽  
Energy Harvesters ◽  
Large Oscillation ◽  
Strong Winds

The performance of recently proposed flag-based energy harvesters is strongly limited by the chaotic response of flags to strong winds. From an experimental point of view, the detection of flag chaotic dynamics were scarce, based on the flapping amplitude and the maximal Lyapunov exponent. In practice, tracking the flapping amplitude is difficult and flawed in the large oscillation limit. Also, computing the maximal Lyapunov exponent from time series of limited size requires strong assumptions on the attractor geometry, without getting insurance of their reliability. For bypassing these issues, (1) we use a time series which takes into account the whole dynamics of the flag, by using the flapping moment which integrates its displacements, and (2) we apply an algorithm of detection of chaos based on recurring values in time series.

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