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EN
The phenomenon of stochastic resonance (SR) in a tumor growth model under the presence of immune surveillance is investigated. Time delay and cross-correlation between multiplicative and additive noises are considered in the system. The signal-to-noise ratio (SNR) is calculated when periodic signal is introduced multiplicatively. Our results show that: (i) the time delay can accelerate the transition from the state of stable tumor to that of extinction, however the correlation between two noises can accelerate the transition from the state of extinction to that of stable tumor; (ii) the time delay and correlation between two noises can lead to a transition between SR and double SR in the curve of SNR as a function of additive noise intensity, however for the curve of SNR as a function of multiplicative noise intensity, the time delay can cause the SR phenomenon to disappear, and the cross-correlation between two noises can lead to a transition from SR to stochastic reverse-resonance. Finally, we compare the SR phenomenon for the multiplicative periodic signal with that for additive periodic signal in the tumor growth model with immune surveillance.
2
Content available remote Stochastic resonance in a realistic model for surface adsorption
100%
Open Physics
|
2012
|
tom 10
|
nr 3
625-630
EN
We study a model for a monolayer single adsorbate system used to describe pattern formation on adsorbates with lateral interactions, when it is submitted to pressure oscillations. Through numerical and analytical (based on a two-state approximation) methods to analyze the existence of stochastic resonance in such a bistable system. This is a first step toward the study of resonant phenomena in adsorbate systems with moving fronts and/or with presence of micro-reactors or spots.
Open Physics
|
2009
|
tom 7
|
nr 3
601-606
EN
The stochastic resonance (SR) phenomenon induced by a multiplicative periodic signal in a logistic growth model with correlated noises is studied by using the theory of signal-to-noise ratio (SNR) in the adiabatic limit. The expressions of the SNR are obtained. The effects of multiplicative noise intensity α and additive noise intensity D, and correlated intensity λ on the SNR are discussed respectively. It is found that the existence of a maximum in the SNR is the identifying characteristic of the SR phenomena. In comparison with the SR induced by additive periodic signal, some new features are found: (1) When SNR as a function of λ for fixed ratio of α and D, the varying of α can induce a stochastic multi-resonance, and can induce a re-entrant transition of the peaks in SNR vs λ; (2) There exhibits a doubly critical phenomenon for SNR vs D and λ, i.e., the increasing of D (or λ) can induce the critical phenomenon for SNR with respect to λ (or D); (3) The doubly stochastic resonance effect appears when α and D are simultaneously varying in SNR, i.e., the increment of one noise intensity can help the SR on another noise intensity come forth.
EN
This study demonstrates how a time domain data based non-linear approach known as Stochastic Resonance (SR) can be effectively used for fault detection in spur gearboxes. SR has just been used recently for fault diagnosis in mechanical systems with a focus on faulty systems. This paper examines the behaviour of SR when it is applied to healthy systems, in particular a healthy gearbox and explores approaches like residual signal and filtered signal computations to aid in the containment of false alarms while improving overall results. Although SR is a time domain procedure, its results also extend to the frequency domain.
5
Content available remote Signal enhancement for detection of wear im machine monitoring applications
75%
EN
The use of stochastic resonance as a signal enhancement technique for tribological process monitoring was investigated. The addition of an appropriate amount of noise to machine vibration signals along with the use of a signal threshold increased initially low level signal components of frequency spectra of vibration signals.
6
Content available remote Stochtyczny rezonans przejściowy konstrukcji wzbudzanej maszyną wirową
63%
EN
The problem of the transient resonance in the stochastic formulation has been considered. After introducing new variables and applying the Ito differentiation formula a set or differential equations for finding the probabilistic moments of the system response have been obtained. The approach is illustrated by calculating the probabilistic moments of the displacements of concrete foundation excited by a vibrating machine.
EN
Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.
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