Conference Paper

Multiscale Cross Entropy: A Novel Algorithm for Analyzing Two Time Series

DOI: 10.1109/ICNC.2009.118 Conference: Natural Computation, 2009. ICNC '09. Fifth International Conference on, Volume: 1
Source: IEEE Xplore

ABSTRACT We proposed and developed a novel algorithm, named multiscale cross entropy (MSCE), to assess the dynamical characteristics of coupling behavior between two sequences on multiple scales, and apply it into the analysis of ¿coupling behavior¿ between two variables in physical and physiological systems, such as Henon-Henon map, Ro¿ssler-Lorenz differential equations and autonomic nervous system. The MSCE analysis, explicitly addressing multiscale features of coupling system, not only provides a nonlinear index of asynchrony at multiple temporal scales, but a measure of fractal dynamical characteristics relative to coupling behavior.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The flow of an oil–water two-phase fluid in an inclined pipe exhibits fundamentally different behaviors to that of a vertical two-phase flow, especially the flow commonly presents complex countercurrent flow structure due to the influence of gravity. The understanding of inclined oil–water flow is of important significance for flow measurement and production optimization. We using multi-scale cross entropy (MSCE) analysis investigate the nonlinear dynamics of inclined water-dominated oil–water two-phase flow patterns which are Dispersion oil-in-water-Pseudoslugs (D O/W PS), Dispersion oil-in-water-Countercurrent (D O/W CT) and Transitional Flow (TF). We find that the rate of low-scale cross entropy can effectively identify flow patterns, and the high-scale cross entropy can represent their long-range dynamics. The research results show that the multi-scale cross entropy analysis can be a helpful tool for revealing nonlinear dynamics of inclined oil–water two-phase flow in terms of microscopic and macroscopic views.
    Chemical Engineering Science 12/2011; 66(23):6099-6108. DOI:10.1016/j.ces.2011.08.034 · 2.61 Impact Factor