Phase‐space footprints of truncated periodicity: Time domain wave‐oriented data processing

1994 ◽  
Vol 95 (5) ◽  
pp. 2903-2903
Author(s):  
L. Carin ◽  
L. B. Felsen ◽  
T.‐T. Hsu ◽  
D. Kralj
Keyword(s):  
Phase Space ◽  
Data Processing ◽  
Time Domain

scholarly journals Time-Domain Scars: Resolving the Spectral Form Factor in Phase Space

2009 ◽  
Vol 102 (15) ◽  
Author(s):  
Thomas Dittrich ◽  
Leonardo A. Pachón
Keyword(s):  
Form Factor ◽  
Phase Space ◽  
Time Domain ◽  
Spectral Form

scholarly journals Bifurcations of a Controlled Two-Bar Linkage Motion with Considering Viscous Frictions

2011 ◽  
Vol 18 (1-2) ◽  
pp. 365-375 ◽  
Author(s):  
Qingkai Han ◽  
Xueyan Zhao ◽  
Xingxiu Li ◽  
Bangchun Wen
Keyword(s):  
Phase Space ◽  
Lyapunov Exponents ◽  
Time Domain ◽  
Shooting Method ◽  
Viscous Friction ◽  
Dynamical Model ◽  
Chaotic Motions ◽  
Amplitude Spectra ◽  
Set Up ◽  
Joint Motions

In this paper, we investigate the joint viscous friction effects on the motions of a two-bar linkage under controlling of OPCL. The dynamical model of the two-bar linkage with an OPCL controller is firstly set up with considering the two joints' viscous frictions. Thereafter, the motion bifurcations of the two-bar linkage along the values of joint viscous frictions are obtained using shooting method. Then, single-periodic, multiple-periodic, quasi-periodic and chaotic motions of link rotating angles are simulated with given different viscous friction values, and they are illustrated in time domain waveforms, phase space portraits, amplitude spectra and Poincare mapping graphs, respectively. Additionally, for the chaotic case, Lyapunov exponents and hypothesis possibilities of the two joint motions are also estimated.

Exterior Ballistic Data Processing by SVD and Wavelet Transform

2012 ◽  
Vol 562-564 ◽  
pp. 1394-1397
Author(s):  
Yu Hua Dong ◽  
Hai Chun Ning
Keyword(s):  
Wavelet Transform ◽  
Phase Space ◽  
Data Processing ◽  
Wavelet Decomposition ◽  
Hankel Matrix ◽  
Singular Value ◽  
Experimental Data Processing ◽  
Abnormal Detection ◽  
Reconstructed Phase Space ◽  
Trajectory Measurement

This paper proposes a method of wavelet transform combined with SVD (Singular Value Extracting), and the abnormal data elimination in its trajectory measurement is studied. After the wavelet decomposition of the observed data, combining the approximate component and the detail component, the phase space is reconstructed. The increment criterion of singular entropy is used for the input observed matrix of SVD, and the singular value is selected. Then the original signal is reconstructed by SVD inverse transform. This method overcomes the distortion problem of data end in phase space reconstruction by Hankel matrix. The reconstructed phase space by components of wavelet decomposition is orthogonal. So it further improves the accuracy of noise reduction and abnormal detection by SVD. The results of experimental data processing show the effectiveness of this method proposed in the paper.

3D prediction of surface-related and interbed multiples

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. V1-V6 ◽  
Author(s):  
Moshe Reshef ◽  
Shahar Arad ◽  
Evgeny Landa
Keyword(s):  
Data Processing ◽  
Time Domain ◽  
Final Section ◽  
Real Data ◽  
Computational Procedure ◽  
Detailed Knowledge ◽  
Geological Model ◽  
Multiple Attenuation ◽  
The Time Domain ◽  
Kinematic Properties

Multiple attenuation during data processing does not guarantee a multiple-free final section. Multiple identification plays an important role in seismic interpretation. A target-oriented method for predicting 3D multiples on stacked or migrated cubes in the time domain is presented. The method does not require detailed knowledge of the subsurface geological model or access to prestack data and is valid for both surface-related and interbed multiples. The computational procedure is based on kinematic properties of the data and uses Fermat's principle to define the multiples. Since no prestack data are required, the method can calculate 3D multiples even when only multi-2D survey data are available. The accuracy and possible use of the method are demonstrated on synthetic and real data examples.

Medicine and the Chaos Theory in Description of Individual and Particular

10.12737/5892 ◽  
2014 ◽  
Vol 21 (3) ◽  
pp. 27-35
Author(s):  
Еськов ◽  
V. Eskov ◽  
Джумагалиева ◽  
L. Dzhumagalieva ◽  
Еськов ◽  
...  
Keyword(s):  
Phase Space ◽  
Time Domain ◽  
Stochastic Systems ◽  
Distribution Functions ◽  
Continuous Change ◽  
Recording Time ◽  
Behavior Dynamics ◽  
Biomedical Systems ◽  
Present Complex ◽  
Self Organizing

The article presents three approaches (deterministic, stochastic and chaotic – self-organizing) for studying biomedical systems. The authors show that complex biosystems cann’t be described by deterministic and stochastics because of constant changing parameters xi of a state vector of such systems x=x(t). The fundamental distinguish of deterministic and stochastic systems from chaotic – self-organizing is continuous movement x(t) in phase space of states. The authors also present complex of objects which the authors have been studying for the last 30 years and which conform the type III systems. The particular features of the personalized medicine are presented, that denies possibility of identification of body state at one measurement (a point in a phase space). It is connected with the fact that there is a uniform distribution x(t) in time-domain xi which is revealed in continuous change of distribution functions f(x) for different discrete recording time-domain x(t) at all xi. The authors assert that behavior dynamics of neural networks is similar to work of neuroemulators that is terminated by certainty in quasi-attractor’s volumes.

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