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Tung W.-W.,Purdue University | Gao J.,PMB Intelligence LLC | Hu J.,Affymetrix | Yang L.,University of Florida
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2011

Detecting chaos and estimating the limit of prediction time in heavy-noise environments is an important and challenging task in many areas of science and engineering. An important first step toward this goal is to reduce noise in the signals. Two major types of methods for reducing noise in chaotic signals are chaos-based approaches and wavelet shrinkage. When noise is strong, chaos-based approaches are not very effective, due to failure to accurately approximate the local chaotic dynamics. Here, we propose a nonlinear adaptive algorithm to recover continuous-time chaotic signals in heavy-noise environments. We show that it is more effective than both chaos-based approaches and wavelet shrinkage. Furthermore, we apply our algorithm to study two important issues in geophysics. One is whether chaos exists in river flow dynamics. The other is the limit of prediction time for the Madden-Julian oscillation (MJO), which is one of the most dominant modes of low-frequency variability in the tropical troposphere and affects a wide range of weather and climate systems. Using the adaptive filter, we show that river flow dynamics can indeed be chaotic. We also show that the MJO is weakly chaotic with the prediction time around 50 days, which is considerably longer than the prediction times determined by other approaches. © 2011 American Physical Society. Source


Gao J.,PMB Intelligence LLC | Gao J.,Wright State University | Hu J.,Affymetrix | Mao X.,University of Florida | Perc M.,University of Maribor
Journal of the Royal Society Interface | Year: 2012

Culturomics was recently introduced as the application of high-throughput data collection and analysis to the study of human culture. Here, we make use of these data by investigating fluctuations in yearly usage frequencies of specific words that describe social and natural phenomena, as derived from books that were published over the course of the past two centuries. We show that the determination of the Hurst parameter by means of fractal analysis provides fundamental insights into the nature of long-range correlations contained in the culturomic trajectories, and by doing so offers new interpretations as to what might be the main driving forces behind the examined phenomena. Quite remarkably, we find that social and natural phenomena are governed by fundamentally different processes. While natural phenomena have properties that are typical for processes with persistent long-range correlations, social phenomena are better described as non-stationary, on-off intermittent or Lévy walk processes. © 2012 The Royal Society. Source


Gao J.,PMB Intelligence LLC | Gao J.,Wright State University | Hu J.,Affymetrix | Tung W.-W.,Purdue University
PLoS ONE | Year: 2011

Background: Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings: To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions: The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals. © 2011 Gao et al. Source


Zhu H.B.,Ningbo University | Gao J.B.,Guangxi University | Gao J.B.,PMB Intelligence LLC
Physica A: Statistical Mechanics and its Applications | Year: 2014

The fractal behavior of traffic flow is studied by the adaptive fractal analysis method on the basis of the vehicle headway time series, which are obtained from the numerical simulation of the NaSch model. We find that the vehicle headway time series has a fractal behavior that is similar to the standard Brownian motion (BM) over a wide range of scales when the density is low. As the density increases well-defined sharp spectral peaks, corresponding to the stop-and-go waves, appear while the scale range showing BM-like behavior rapidly shrinks. In the high density regime, a new type of fractal behavior with long-range correlations appears, accompanying the worsening of traffic congestions. The underlying dynamics of traffic flow is analyzed, and some meaningful results are obtained. © 2013 Elsevier B.V. All rights reserved. Source


Jiang M.Q.,CAS Institute of Mechanics | Ling Z.,CAS Institute of Mechanics | Meng J.X.,CAS Institute of Mechanics | Gao J.B.,PMB Intelligence LLC | Dai L.H.,CAS Institute of Mechanics
Scripta Materialia | Year: 2010

We report an intriguing observation that the interaction of brittle nanoscale periodic corrugations (NPCs) can lead to the formation of ductile dimples on the dynamic fracture surface of a tough Vit 1 bulk metallic glass (BMG) under high-velocity plate impact. A "beat" phenomenon due to superposition of simple harmonic vibrations, approximately characterizing NPCs, is proposed to explain this unusual brittle-to-ductile transition. The present results agree well with our previously revealed energy dissipation mechanism in the fracture of BMGs. © 2010 Acta Materialia Inc. Source

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