Almost Sure Synchronization of Multilayer Networks via Intermittent Pinning Noises: A White-Noise-Based Time-Varying Coupling

2021 ◽  
pp. 1-14
Author(s):  
Sen Li ◽  
Yuhang Zheng ◽  
Huan Su
Keyword(s):  
White Noise ◽  
Time Varying ◽  
Multilayer Networks ◽  
Almost Sure Synchronization

Time Varying Loads and White Noise on an SDOF System with Bouc Hysteresis Using Gaussian Closure

2019 ◽  
Vol 27 (01) ◽  
pp. 1850062
Author(s):  
Holger Waubke ◽  
Christian Kasess
Keyword(s):  
White Noise ◽  
Gaussian White Noise ◽  
Time Varying ◽  
Time Step ◽  
Closure Technique ◽  
Sdof System ◽  
The Monte Carlo Method ◽  
Step Procedure ◽  
Gaussian White Noise Process ◽  
Gaussian Closure

In a recent publication [H. Waubke and C. Kasess, Gaussian closure technique applied to the hysteretic Bouc model with nonzero mean white noise excitation, J. Sound Vibr. 382 (2016) 258–273], the response of a single-degree-of-freedom (SDOF) system under Gaussian white noise and a constant dead load is presented. The system has a hysteresis described by Bouc [R. Bouc, Forced vibration of mechanical systems with hysteresis, in Proc. Fourth Conference on Nonlinear Oscillation (Prague, 1967), p. 315]. New is the usage of a slowly time-varying deterministic load added to the Gaussian white noise process. The transient solution is calculated using the Gaussian closure technique together with an explicit time step procedure. All moments in the Gaussian closure technique are evaluated analytically. The results of the Gaussian closure technique are in good agreement with the results from the Monte-Carlo method.

Temporal variations in the pattern of breathing

1996 ◽  
Vol 80 (4) ◽  
pp. 1079-1087 ◽  
Author(s):  
E. N. Bruce
Keyword(s):  
White Noise ◽  
Breathing Pattern ◽  
Respiratory Rhythm ◽  
Temporal Variations ◽  
Feedback Loops ◽  
Respiratory Pattern ◽  
Nonlinear Interactions ◽  
Time Varying ◽  
Rhythm Generation ◽  
Physiological Processes

Breath-to-breath variations in the pattern of breathing can occur as uncorrelated random variations (“white noise”), correlated random changes, or as one of two types of nonrandom variations: periodic oscillations or nonrandom nonperiodic fluctuations. White noise is probably present in all physiological processes. In many cases, periodic variations are due to oscillations originating in chemoreflex feedback loops. It has long been hypothesized that correlated random variations in breathing pattern are due to central neutral “memory mechanisms, but part of this behavior might be due to chemoreflex mechanisms. Recently it has been concluded that nonlinear interactions between pulmonary and airway afferent activities and integrative central respiratory mechanisms can produce nonrandom nonperiodic (and also periodic) variability of the respiratory pattern. These latter studies have provided new insights about the behavioral relevance of the integrative character of central respiratory mechanisms and the time-varying nature of pulmonary afferent activities and have emphasized the importance of identifying the physiological bases for these phenomena. These and other findings are interpreted assuming that respiratory rhythm generation/pattern formation occurs via a nonlinear oscillator, and novel inferences concerning temporal variations of the breathing pattern are proposed.

scholarly journals Centralities in High Order Networks

2018 ◽  
Author(s):  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Directed Graph ◽  
Shortest Paths ◽  
High Order ◽  
Time Varying ◽  
Multilayer Networks ◽  
Straightforward Manner

We propose a method for computing centralities based on shortest paths in time-varying, multilayer, and time-varying multilayer networks using MultiAspect Graphs (MAG). Thanks to the MAG abstraction, these high order networks are represented in a way that is isomorphic to a directed graph. We then show that well-known centrality algorithms can be adapted to the MAG environment in a straightforward manner. Moreover, we show that, by using this representation, pitfalls usually associated with spurious paths resulting from aggregation in time-varying and multilayer networks can be avoided.

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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