Pei-ling Zhou

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In this paper, we investigate the dynamical properties of electroencephalogram (EEG) signals of humans in sleep. By using a modified random walk method, we demonstrate that scale-invariance is embedded in EEG signals after a detrending procedure is applied. Furthermore, we study the dynamical evolution of the probability density function (PDF) of the(More)
A model of a Boolean game with only one free parameter p that denotes the strength of local interaction is proposed wherein each agent acts according to the information obtained from his neighbors in the network, and those in the minority are rewarded. The simulation results indicate that the dynamic of the system is sensitive to network topology, whereby(More)
An artificial stock market is established with the modeling method and ideas of cellular automata. Cells are used to represent stockholders, who have the capability of self-teaching and are affected by the investing history of the neighboring ones. The neighborhood relationship among the stockholders is the expanded Von Neumann relationship , and the(More)
Over 40 studies on shadow price estimation for undesirable outputs are reviewed. The general shadow pricing framework is described. We summarize the main methodological and application aspects of existing studies. The shadow price estimation of CO 2 emissions received increasing attention. The regions concerned were shifting from western to Asian countries.(More)
In this paper the diffusion entropy technique is applied to investigate the scaling behavior of financial markets. The scaling behaviors of four representative stock markets, Dow Jones Industrial Average, Standard&Poor 500, Heng Seng Index, and Shang Hai Stock Synthetic Index, are almost the same; with the scale-invariance exponents all in the interval(More)
In this Letter, we investigate an artificial traffic model on scale-free networks. Instead of using the routing strategy of the shortest path, a generalized routing algorithm is introduced to improve the transportation throughput, which is measured by the value of the critical point disjoining the free-flow phase and the congested phase. By using the(More)
In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete(More)
An artificial stock market is established based on multi-agent modeling method. Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. The " market data-like " time series are generated(More)
We sought to analyze the dynamic properties of brain electrical activity from healthy volunteers and epilepsy patients using recurrence networks. Phase-space trajectories of synchronous electroencephalogram signals were obtained through embedding dimension in phase-space reconstruction based on the distance set of space points. The recurrence matrix(More)
The brain functional networks derived from multi-channel ERP signals are analyzed based on the phase synchronization theory. The nodes of brain functional networks are represented by the channels of ERP signals, and the connectivity of brain functional networks is described by the interaction among the channels. The edge between two different channels(More)