Influence Of White Gaussian Noise Research Articles

Multi-feature Extraction of Metal Cracks using Based on Wavelet Neural network

The traditional ultrasonic testing mostly uses a single feature, which induce the inaccuracy of the crack detection. This paper proposes a metal crack detection method based on multi-feature extraction. First, the characteristics of time-frequency information of the signal obtained by using wavelet packet transform is briefly introduced. Secondly, the designed wavelet neural network model consists of first layer with three neurons (wavelet packet transform of the ultrasonic echo signal, the peak and energy function), and the output layer with one neuron presenting recognition features to identify and analyze metal cracks. The experimental results show ultrasonic echo measurement with the improved method proposed model can effectively reduce the influence of white gaussian noise while significantly improving the recognition rate of metal cracks.

A deconvolution method for signal recovery of electromagnetic field sensors

The broadband pulsed electromagnetic (EM) field measurement is significant in the analysis of electromagnetic compatibility. As an efficient measurement method, the D-Dot sensor has good performance. However, the integration method sometimes shows flaws due to the non-ideal frequency response or noise. We propose a deconvolution method based on the Maximum A Posteriori method (MAP). In the practical signal recovery test, the method performs better than the integration method. The adaptability is also tested on four types of different signals. Furthermore, we discuss the influence of white Gaussian noise (WGN) and quantization noise. The relationship between the relative error and the signal-to-noise ratio (SNR) of WGN is given. When the SNR of WGN is greater than 30 dB, the relative error can be reduced under 10%. Besides, when the SNR of quantization noise is greater than 30 dB, the relative error can be reduced to under 30%.

Noise-induced chaotic-attractor escape route

In the present article, a combination of numerical and experimental studies is undertaken to comprehend the influence of noise on the responses of continuous-time dynamical systems. In particular, the influence of white Gaussian noise on the chaotic and periodic responses of bistable, Duffing oscillators is the focus of this work. The noteworthy result of the conducted studies concerns the presence of a pair of attractors, one being periodic and the other being chaotic: the chaotic attractor response can be controlled and terminated with an appropriate noise level. For trajectories in the basin of the chaotic attractor, white Gaussian noise is added at a barely sufficient level to allow trajectories to eventually leave (within some specified time). The authors report that trajectories leave via a special escape route: the unstable manifold of a fixed point saddle on the basin boundary between the two basins of attraction. Striking similarities and differences between experimental and numerical investigation are discussed in the work.

Influence of noise on frequency responses of softening Duffing oscillators

In the present study, the influence of noise on the responses of continuous-time dynamical systems are considered. In particular, the influence of white Gaussian noise on the frequency responses of monostable and bistable, softening Duffing oscillators are studied. A combination of experimental, analytical, and numerical studies are undertaken to understand the shifting of jump-up and jump-down frequencies and an eventual collapse of upper and lower responses branches of into one response curve with no jump instabilities for the considered Duffing oscillator. It is noted that with noise, the hysteresis observed in the response of the nonlinear oscillator without noise can be destroyed.

Hysteretic behavior of spin-crossover noise driven system

The influence of white Gaussian noise on hysteretic behavior of spin-crossover system is analyzed in the framework of stochastic Langevin dynamics. Various stochastic simulations are performed and several important properties of spin-transition in spin-crossover system driven by noise are reproduced. The numerical results are tested against the stationary probability function and the associated dynamic potential obtained from Fokker–Planck equation corresponding to spin-crossover Langevin dynamics. The dependence of light-induced optical hysteresis width and non-hysteretic transition curve slope on the noise intensity is illustrated. The role of low-spin and high-spin phase stabilities in the hysteretic behavior of noise-driven spin-crossover system is discussed.

A modified direction-of-arrival estimation algorithm for acoustic vector sensors based on Unitary Root-MUSIC

A novel method applying for direction-of-arrival(DOA) using acoustic vector sensors(AVS) based on Unitary Root-MUSIC algorithm(URM) is proposed in this paper. AVS array has a characteristic named coherence principle of sound pressure and velocity, which can significantly improve the detection performance of DOA by reducing the influence of white Gaussian noise. We apply this characteristic and the extra velocity information of AVS to construct a modified covariance matrix. In particular, the modified covariance matrix need not extend the dimension in calculation of AVS covariance matrix which means saving the computing time. In addition, we combine the characteristics of modified matrix with URM algorithm to design a new algorithm, which can minimize the impact of environment noise and further reduce computational complexity to a lower order of magnitude. So the proposed method can not only improve the accuracy of DOA detection but also reduce the computational complexity, compared to the classic DOA algorithm. Theory analysis and simulation experiment show that the proposed algorithm for AVS based on URM can significantly improve the DOA resolution in low SNR ratios and few snapshots.

The effect of Gaussian white noise on the fractality of fluctuations in the plasma of a symmetrical discharge

In this work we investigate the influence of white Gaussian noise on the fluctuations in the plasma of a symmetrical discharge using multifractal detrended fluctuation analysis. We observe that in the range of noise intensity used in our study, the multifractality strength is increased by the noise, at all values of the inter-anode voltage, both for original and filtered time-series. This is interpreted as a new positive influence of noise because this effect can be understood as an increasing in the predictability on the dynamics in a time-series. A constructive influence of noise can appear only for fluctuations with underlying chaotic dynamics. The shuffling analysis demonstrates that the multifractality is purely a consequence of the correlations of the fluctuations. The noise influence is also observed in the change of the position of the maximum in the singularity spectra. The multifractal detrended cross correlation between light intensity and current intensity demonstrates that the fluctuations in both parameters are generated by the same physical processes though they are very different in nature: one is a local parameter and the other is a global one.

Noise-induced attractor annihilation in the delayed feedback logistic map

We study dynamics of the bistable logistic map with delayed feedback, under the influence of white Gaussian noise and periodic modulation applied to the variable. This system may serve as a model to describe population dynamics under finite resources in noisy environment with seasonal fluctuations. While a very small amount of noise has no effect on the global structure of the coexisting attractors in phase space, an intermediate noise totally eliminates one of the attractors. Slow periodic modulation enhances the attractor annihilation.

Fault Identification Based on Nlpca in Complex Electrical Engineering

Fault Identification Based on Nlpca in Complex Electrical Engineering The fault is inevitable in any complex systems engineering. Electric power system is essentially a typically nonlinear system. It is also one of the most complex artificial systems in this world. In our researches, based on the real-time measurements of phasor measurement unit, under the influence of white Gaussian noise (suppose the standard deviation is 0.01, and the mean error is 0), we used mainly nonlinear principal component analysis theory (NLPCA) to resolve fault identification problem in complex electrical engineering. The simulation results show that the fault in complex electrical engineering is usually corresponding to the variable with the maximum absolute value coefficient in the first principal component. These researches will have significant theoretical value and engineering practical significance.

When does noise destroy or enhance synchronous behavior in two mutually coupled light-controlled oscillators?

We study the influence of white gaussian noise in a system of two mutually coupled light-controlled oscillators (LCOs). We show that under certain noise intensity conditions, noise can destroy or enhance synchronization. We build some Arnold tonguelike structures in order to explain the effects due to noise. It is remarkable that noise-enhanced synchronization is possible only when the variances of the noise acting on each of the LCOs are different.

Amplitude Estimation by a Multipoint Interpolated DFT Approach

This paper focuses on the amplitude estimation by a multipoint interpolated discrete Fourier transform (DFT) method. Accurate results are obtained using the weighted multipoint interpolated DFT (WMIpDFT) method with maximum sidelobe decay windows. In addition, using the WMIpDFT method with maximum sidelobe decay windows, it is mathematically proven that the systematic errors affecting the amplitude estimation decrease as the number of interpolation points and/or the window order increases. Computer simulations confirm the accuracy of the derived expressions. Moreover, the influence of white Gaussian noise on the amplitude estimations has been analyzed by means of computer simulations.