Quasi-periodic events on structured earthquake models*

Abstract

There has been much interest in studying quasi-periodic events on earthquake models. Here we investigate quasi-periodic events in the avalanche time series on structured earthquake models by the analysis of the autocorrelation function and the fast Fourier transform. For random spatial earthquake models, quasi-periodic events are robust and we obtain a simple rule for a period that is proportional to the choice of unit time and the dissipation of the system. Moreover, computer simulations validate this rule for two-dimensional lattice models and cycle graphs, but our simulation results also show that small-world models, scale-free models, and random rule graphs do not have periodic phenomena. Although the periodicity of avalanche does not depend on the criticality of the system or the average degree of the system or the size of the system, there is evidence that it depends on the time series of the average force of the system.

Experimental analysis of interface contact behavior using a novel image processing method*

“…Image information is extracted by Gaussian filter. [40][41][42] The centrosymmetric spectrum is obtained by two-dimensional Fourier transform of grayscale image and shift, expressed as…”

Power-law statistics of synchronous transition in inhibitory neuronal networks

“…When the probability of measuring a particular value of some parameter varies inversely as a power of that value, the parameter is said to obey a power law. [1,2] Power-law statistics are ubiquitous in physics and life sciences [3][4][5][6][7] and occur in an extraordinarily diverse range of phenomena, from earthquakes [8][9][10] to forest fires, [11] the numbers of species in biological taxa, [12] the number of citations received by papers, [13] the number of hits on web pages. [14] These distributions reveal unusual properties: The observed parameter has no typical scale, for instance, size, duration, and shape of collective phenomena.…”