Synchronization of dissipative dynamical systems driven by non-Gaussian Lévy noises

Dynamical systems driven by Gaussian noises have been considered extensively in modeling, simulation, and theory. However, complex systems in engineering and science are often subject to non-Gaussian fluctuations or uncertainties. A coupled dynamical system under a class of Lévy noises is considered. After discussing cocycle property, stationary orbits, and random attractors, a synchronization phenomenon is shown to occur, when the drift terms of the coupled system satisfy certain dissipativity and integrability conditions. The synchronization result implies that coupled dynamical systems share a dynamical feature in some asymptotic sense

NUMERICAL SCHEME FOR BACKWARD DOUBLY STOCHASTIC DIFFERENTIAL EQUATIONS WITH TIME DELAYED COEFFICIENTS

In this paper, we present some assumptions to get the numerical scheme for backward doubly stochastic dierential delay equations (shortly-BDSDDEs), and we propose a scheme of BDSDDEs and discuss the numerical convergence and rate of convergence of our scheme

A new bearing fault diagnosis scheme using MED-morphological filter and ridge demodulation analysis

For rolling bearing diagnosis, the major challenge of signal processing technique is to extract the quasi-periodic impulses which generated by rolling bearing fault, especially when rolling bearing operated in the condition of heavy noise. This paper proposed a new bearing fault diagnosis scheme. First, the Minimum Entropy Deconvolution (MED) is taken to obtain the impulse excitations from the bearing vibration signal. Then, two kinds of morphological filter, named average filter(AVG) and difference filter (DIF), are used as the assisted filtering unit to reduce the random noise in original signal and integrate the positive and negative impulse excitations in MED filtered signal, respectively. At last, the STFT based ridge demodulation analysis is applied to the purified signal, and the bearing fault is easily identified by spectral analysis of the demodulated signal. Two simulated signal are analyzed to test the performance of the proposed scheme. In the first case, the periodic impulse signal adding with random noise is analyzed. The result shows that MED-AVG-DIA is the best scheme for impulse feature extraction. In the second case, the pure impulse signal which filtered by MED is analyzed. The result shows that STFT based ridge demodulation analysis can achieve better demodulation effect than other demodulation methods. The proposed fault diagnosis scheme has been further verified by simulation signal and measured vibration signals of defective bearing. The result shown that the proposed scheme is feasible and effective for the fault diagnosis of rolling bearing

Active Multiple Plasmon-Induced Transparency with Graphene Sheets Resonators in Mid-Infrared Frequencies

A multiple plasmon-induced transparency (PIT) device operated in the mid-infrared region has been proposed. The designed model is comprised of one graphene ribbon as main waveguide and two narrow graphene sheets resonators. The phase coupling between two graphene resonators has been investigated. The multimode PIT resonances have been found in both cases and can be dynamically tuned via varying the chemical potential of graphene resonators without optimizing its geometric parameters. In addition, this structure can get multiple PIT effect by equipping extra two sheets on the symmetric positions of graphene waveguide. The simulation results based on finite element method (FEM) are in good agreement with the resonance theory. This work may pave new way for graphene-based thermal plasmonic devices applications

Forecasting mortality patterns of thalassaemia major patients in Iraq by using VAR model and reasons for this mortality

The vector autoregression model (VAR) is a natural extension of the univariate autoregressive model dynamic multivariable time series. It is one of the most successful, flexible, and easy to use models for the analysis of multivariable time series. The VAR model has proved to be particularly useful describing the dynamic behaviour of economic and financial time series and forecasting. Often it provides superior forecasts to those of time-series models and univariate and detailed forecasts, based on the theory of simultaneous equation models. Expectations of VAR models are very flexible because they can be conditioned on possible paths for the future in the form of specific variables. In addition to describing the data and forecasting, the VAR model is used to deduce structural and policy analysis. This study used the VAR model for forecasting the number of deaths in patients with thalassemia in Maysan province in southern Iraq, and also addressed the causes of these deaths. There was a strong relationship between mortality in thalassemia patients and an increase in the proportion of iron and the highest number of deaths was recorded for patients who had a very high proportion of iron. It was „the most important cause of mortality (Cardiac disease, infections, the liver, the spleen)