Features stochastics and chaos theory processing myogram

2015 ◽  
Vol 4 (1) ◽  
pp. 45-53
Author(s):  
Горбунов ◽  
D. Gorbunov ◽  
Эльман ◽  
Kseniya Elman ◽  
Гавриленко ◽  
...  
Keyword(s):  
Phase Space ◽  
Shannon Entropy ◽  
Chaos Theory ◽  
Muscle Tension ◽  
Quantitative Measure ◽  
Biological Research ◽  
Heavy Load ◽  
Dimensional Phase Space ◽  
Weak Muscle ◽  
Little Finger

In studies using the method of multi-dimensional phase space. In the study and modeling of complex biological objects (complexity) there is the possibility of introducing traditional physical methods in biological research and new methods based on chaos theory, self-organization. The paper shows the feasibility of applying the method of multi-dimensional phase space as a quantitative measure for the evaluation of chaotic dynamics on the example of the muscles (flexor of the little finger). As a measure of the state of the neuromuscular system of the person (weak muscle tension and strong, almost the maximum force) used quasi-attractors volumes of multidimensional phase space. This enables identification of the actual measurements of the parameters of the functional state with weak muscles (p = 5th Dan) and strong (P = 10 daN) static stress. Was built timebase signal obtained with myograph and were built autocorrelation function A (t) signal. In the end analysis of the biomechanical system based on a comparison of volume quasi-attractor, as well as on the basis of analysis of the Shannon entropy N. kzvaziattraktora volume displacement at low load is slightly less than the same amount of displacement under a heavy load of flexor muscles of the little finger, just as the values of the Shannon entropy at a heavy load is increased as compared with the values obtained by the weak muscle load.

Analysis of Myograms Acording to the Stochastics and the Chaos Theory – Self-Organization

2015 ◽  
Vol 22 (2) ◽  
pp. 32-38
Author(s):  
Григоренко ◽  
V. Grigorenko ◽  
Горбунов ◽  
D. Gorbunov ◽  
Еськов ◽  
...  
Keyword(s):  
Phase Space ◽  
Shannon Entropy ◽  
Chaos Theory ◽  
The State ◽  
Biomechanical Analysis ◽  
Self Organization ◽  
Biological Research ◽  
Dimensional Phase Space ◽  
Low Load ◽  
Little Finger

The paper shows the feasibility of applying the method of multi-dimensional phase space as a quantitative measure for the evaluation of chaotic dynamics on the example of the muscles (flexor of the little finger). The method of multi-dimensional phase space was used. In the study and modeling of complex biological objects (complexity) there is the possibility of introducing traditional physical methods in biological research and new methods based on the chaos theory and self-organization. As a measure of the state of the neuromuscular system of the person (weak muscle tension and strong, almost the maximum force), the authors used quasi-attractors volumes of multidimensional phase space. This enables identification of the actual measurements of the parameters of the functional state with weak muscles (Fi = 5 daN) and strong (Fi = 10 daN) static stress. The authors built a timebase signal received from the electromyograph and the autocorrelation function A(t) of signal. A biomechanical analysis of the state of the system is carried out on the basis of comparison of the volume VG quasi attractor, as well as on the basis of the analysis of the Shannon entropy E. Volume of quasi attractor VG displacements under low load is slightly less than the same volume displacement VG with strong exertion of the muscles of the flexor of the little finger, exactly the same as the values of the Shannon entropy under a heavy load increases compared to the values obtained under low load the muscles.

Understanding the Unpredictability of Cancer using Chaos Theory and Modern Art Techniques

Leonardo ◽  
2016 ◽  
Vol 49 (1) ◽  
pp. 66-67
Author(s):  
Dhruba Deb
Keyword(s):  
Phase Space ◽  
Conceptual Model ◽  
Chaotic System ◽  
Chaos Theory ◽  
Strange Attractor ◽  
Model Building ◽  
Modern Art ◽  
System Level ◽  
Abstract Expressionist ◽  
Dimensional Phase Space

The unpredictability of cancer poses a threat to personalized cures. Although cancer is studied as a chaotic system, the shape of its unpredictability, known as the strange attractor, is unclear. In this article, the author discusses a conceptual model, building on the strange attractor in cancer phase space. Using techniques of cubism, the author defines the 10-dimensional phase space and then, using an abstract expressionist approach, represents the strange attractor, which twists and turns in multi dimensions, indicating the unpredictability of cancer. This conceptual model motivates the identification of specific experiments for a system-level understanding of cancer.

scholarly journals Local Functional Coefficient Autoregressive Model for Multistep Prediction of Chaotic Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Liyun Su ◽  
Chenlong Li
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Chaos Theory ◽  
Nearest Neighbor ◽  
Simulated Data ◽  
Real Data ◽  
Chaotic Time Series ◽  
Dimensional Phase Space ◽  
Local Functional ◽  
Functional Coefficient

A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed inm-dimensional phase space with a time delayτby using chaos theory. Secondly, select the nearest neighbor points by using local method in them-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.

Statistical and evaluation chaotic parameters under cardio exertion

2015 ◽  
Vol 4 (2) ◽  
pp. 5-10
Author(s):  
Филатова ◽  
D. Filatova ◽  
Карпин ◽  
Vladimir Karpin ◽  
Еськов ◽  
...  
Keyword(s):  
Phase Space ◽  
Cardiovascular System ◽  
Chaos Theory ◽  
Chaotic Dynamics ◽  
Stochastic Approach ◽  
Dynamic Exercise ◽  
Self Organization ◽  
Dimensional Phase Space ◽  
Classical Statistics ◽  
Fitness Indicators

Methods of classical statistics and the theory of chaos and self-organization studied the behavior of the vector of the cardiovascular system in groups of students trained and untrained in response to dosed physical stress .It was found that students without physical fitness indicators of cardio area quasi-attractors increased after exercise . The study had shown significant changes in the dynamics of the behavior of the parameters of functional systems of the human body compared to the stochastic approach based on the histogram and Shannon entropy . It is shown the feasibility of application of chaos theory, self-organization in the evaluation of the reaction of the cardiovascular system of the person on the dynamic exercise. As a measure of the cardiovascular system of the person (to load and after the load) used quasi-attractor motion of the state vector of the system in the two-dimensional phase space of states. Within the framework of the theory of chaos and self-organization may determine the parameters of the spacecraft for individual subjects and their groups to compare their chaotic dynamics in time or in the phase space of states.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

scholarly journals Geometric particle-in-cell methods for the Vlasov–Maxwell equations with spin effects

2021 ◽  
Vol 87 (3) ◽  
Author(s):  
Nicolas Crouseilles ◽  
Paul-Antoine Hervieux ◽  
Yingzhe Li ◽  
Giovanni Manfredi ◽  
Yajuan Sun
Keyword(s):  
Phase Space ◽  
Maxwell Equations ◽  
Stimulated Raman Scattering ◽  
Extended Phase ◽  
Five Dimensions ◽  
Particle In Cell ◽  
Spin Effects ◽  
Dimensional Phase Space ◽  
Spin Polarized ◽  
Underdense Plasma

We propose a numerical scheme to solve the semiclassical Vlasov–Maxwell equations for electrons with spin. The electron gas is described by a distribution function $f(t,{\boldsymbol x},{{{\boldsymbol p}}}, {\boldsymbol s})$ that evolves in an extended 9-dimensional phase space $({\boldsymbol x},{{{\boldsymbol p}}}, {\boldsymbol s})$ , where $\boldsymbol s$ represents the spin vector. Using suitable approximations and symmetries, the extended phase space can be reduced to five dimensions: $(x,{{p_x}}, {\boldsymbol s})$ . It can be shown that the spin Vlasov–Maxwell equations enjoy a Hamiltonian structure that motivates the use of the recently developed geometric particle-in-cell (PIC) methods. Here, the geometric PIC approach is generalized to the case of electrons with spin. Total energy conservation is very well satisfied, with a relative error below $0.05\,\%$ . As a relevant example, we study the stimulated Raman scattering of an electromagnetic wave interacting with an underdense plasma, where the electrons are partially or fully spin polarized. It is shown that the Raman instability is very effective in destroying the electron polarization.

scholarly journals Dynamical properties and extremes of Northern Hemisphere climate fields over the past 60 years

2017 ◽  
Vol 24 (4) ◽  
pp. 713-725 ◽  
Author(s):  
Davide Faranda ◽  
Gabriele Messori ◽  
M. Carmen Alvarez-Castro ◽  
Pascal Yiou
Keyword(s):  
Phase Space ◽  
Sea Level ◽  
Northern Hemisphere ◽  
Atmospheric Dynamics ◽  
Complex Nature ◽  
Human Welfare ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Dimensional Phase Space ◽  
Dynamical Properties

Abstract. Atmospheric dynamics are described by a set of partial differential equations yielding an infinite-dimensional phase space. However, the actual trajectories followed by the system appear to be constrained to a finite-dimensional phase space, i.e. a strange attractor. The dynamical properties of this attractor are difficult to determine due to the complex nature of atmospheric motions. A first step to simplify the problem is to focus on observables which affect – or are linked to phenomena which affect – human welfare and activities, such as sea-level pressure, 2 m temperature, and precipitation frequency. We make use of recent advances in dynamical systems theory to estimate two instantaneous dynamical properties of the above fields for the Northern Hemisphere: local dimension and persistence. We then use these metrics to characterize the seasonality of the different fields and their interplay. We further analyse the large-scale anomaly patterns corresponding to phase-space extremes – namely time steps at which the fields display extremes in their instantaneous dynamical properties. The analysis is based on the NCEP/NCAR reanalysis data, over the period 1948–2013. The results show that (i) despite the high dimensionality of atmospheric dynamics, the Northern Hemisphere sea-level pressure and temperature fields can on average be described by roughly 20 degrees of freedom; (ii) the precipitation field has a higher dimensionality; and (iii) the seasonal forcing modulates the variability of the dynamical indicators and affects the occurrence of phase-space extremes. We further identify a number of robust correlations between the dynamical properties of the different variables.

Sign in / Sign up

Export Citation Format

Share Document