Papers by Keyword: White Noise

Abstract: Additive Gauss white noise is one of the most commonly observed interferences in practical engineering applications. This paper proposed an algorithm for the adaptive determination of the optimal wavelet decomposition level based on Jarque-Bera test in efforts to solve the filtering problem of additive white Gaussian noise signal. By, The optimal decomposition level of wavelet is determined by testing the white noise which was realized by calculating skewness (S) and kurtosis (K) of the parameters. With signal-to-noise ratio (SNR) as the measurement index, simulation results show that the presented algorithm have higher accuracy, and better filtering effect on low SNR signals compared with nonparametric test methods.

Abstract: This study aims to analyze the effect of Marr-Pirt model driven by white and integrated noises. The Gaussian white noise and integrated noises are generated using the Box-Muller algorithm. It is observed that noises could change the peak value and time of the model. In addition, integrated noises have more significant impact on the model than white noise when they are under the same intensity. This reveals integrated noises can better approximate to a practical stochastic process, and deserve substantial attention.

Abstract: One of the challenges in MEMS era is dynamical modeling and control. This paper presents the analysis and control of drive mode of an 80 mm-thick, 1.1 mm in diameter high aspect-ratio (20:1) ring vibratory MEMS gyro. At first dynamic model of MEMS is derived through energy and Lagrange equations. Then state space equation is derived. Natural frequencies were derived by ABAQUS software and through equations. Frequency analysis was applied to analyze system and making comparison between two applied approaches in deriving frequencies. At last modified PID control was designed based on previous section results. We extracted results in presence of white noise to examine stability of the proposed controller. Consequently drive mode shows acceptable behavior through the applied PID controller. The aim is accomplished, though more advanced control can be applied to reduce uncertainty and compensate the noise in feedback to around zero.

Abstract: The partial discharge (PD) detection systems are often vulnerable to strong external interferences, and sometimes the PD signals are submerged in noises (white noise for example) completely. So the signals acquired must be preprocessed to obtain the reliable PD information. While there are many methods for white noise denoising, mostly are not very suitable for partial discharge. The wavelet transform (WT) coefficient of PD and white noises have different spread characteristics in different WT scales. Based on the Information Theory, The Minimum Information Description Length (MDL) criterion is a optimization strategy, a small amount of signal parameter is requried to the PD signals representation, the paper proposes a wavelet spatial correlation algorithm to partial discharge denoising based on MDL criterion: optimal wavelet function is selected based on MDL, then have the white noise reduced in WT, the algorithm has wonderful virtues such as free from any parameters estimation about noise, free from presetting threshhold and threshold chooseing behavior, so the algorithm is highly adaptive. Large amount of experimental results illustrate that the method presented in this paper are efficient and feasible and outperforms other general method of PD noise reduction.

Abstract: For the limitations of HHT of the internal combustion engine vibration signal analysis, and the problem of WVD cross-term suppression methods existing aggregation and cross-term component suppression conflicting, the time-frequency analysis method based on EMD white noise energy density distribution characteristics of the internal combustion engine vibration is proposed. First, the internal combustion engine vibration signal was decomposed into the independent series intrinsic mode function (IMF) with different characteristic time scales by using EMD decomposition method. Then, based on the energy density distribution characteristics of the white noise in EMD decomposition, used the distribution interval estimation curve of the IMFs energy density logarithm of white noise with the same length of the original signal as cordon for false pattern component, identified and eliminated false mode component of vibration signal IMFs component, analysised of each IMF with Wigner-Ville. Finally, the Wigner-Ville analysis results of each IMF were linear superposed in order to reconstruct the original signal time-frequency distribution. Simulation and engine vibration time-frequency analysis results show that this method has an excellent time-frequency characteristics, and can successfully extract feature information of the internal combustion engine cylinder head vibration signal.

Abstract: These precision instruments and equipments install inside the high-tech factory buildings have highly tendency of sensitiveness to ambient vibration; therefore, the criteria of ambient vibration have extremely strict requirements in relatively. For comply ambient vibration to have in accordance with the vibration standards, access the principle of increasing the product defect-free rate, the most common solution is applied by enhance the strength of its earth ground base. Nowadays the foundation base design of high-tech factory buildings is not like Seismic Design can be calculated by formulas and acquired the advance ambient vibration results from the software simulations; hence after the finalization of the factory building construction, it is necessary to have in-filed detection with inspection to makeup these improvements. As a matter of fact the weak signal can be hardly detected, so it reply on specific sensor to transform the signal into low current or low voltage then use an amplifier to magnify its amplitude; in the meanwhile, it not only enlarge the required signal but also the unnecessary noise in the whole process. This study apply Ensemble Empirical Mode Decomposition (EEMD) method to analyze the original signal, and the mean period of the Intrinsic Mode Function (IMF) derived partial components to proceed Fast Fourier Transform (FFT) transformation in order to get partial signal frequency; hereafter apply the white noise to substitute the first signal then reorganized it as the source of ambient vibration signal, a comparison is being made between the background vibration value of its actual measurement and the earth ground base response of its real measurement value. It anticipates having control the influential value of ambient vibration of the construction under the phase when design the factory building in order to elevate the reliable degree of foundation construction design in the future.

Abstract: In order to obtain new ways of weak signal detection, we analyzed the motion states of nonlinear dynamic model Duffing oscillator in the case of different amplitude of input signals by solving the Duffing equation and expounded the basic principles of weak signal detection based on Duffing oscillator phase-change characteristics, further illustrated the relationship between signal detection accuracy and detection time by the experimental, researched the impact on signal detection coming from Gaussian white noise and also pointed out how to use intermittent chaos state to implement weak signal detection. The results showed that Duffing oscillator can be effectively detect the slight changes of input signal in the strong noises background, so as to achieve the purpose of weak signal detection. Compared with existing methods, it could greatly improve the detection results.

Abstract: Signal transmission is often subject to the disturbance of white noise. Owing to the spectrum of white noise can be found in the real number field, it is often difficult to filter out with the traditional filter. This article describes the methods of white noise suppression using adaptive filter and mean filter. First, using the genetic algorithm to optimize the weight vector of the adaptive filter, and then using the method of the mean filter to further filter, Simulation results show that the filter can effectively suppress white noise.

Abstract: Aiming at relieving the mode mixing of the intrinsic time-scale decomposition (ITD) method used to process the intermittent signals, the improved intrinsic time-scale decomposition (IITD) method is proposed in the paper. The IITD method employs white noise which has the uniform power distribution in different frequencies as a reference to resolve the problem. Furthermore, a simulation signal was utilized to examine the performance of the IITD method as well as that of the original ITD method. As a result, the comparison between the results from the two methods indicates that the IITD method reflects an obvious improvement on the original ITD method for processing the intermittent signals.

Abstract: The modified Levenberg-Marquardt (mLM) method is introduced for nonlinear parametric system, such as stiffness proportional damping and Rayleigh proportional damping. Since the mLM method is sensitive to the initial values of parameter, a SVD-mLM method is proposed with combination of singular value decomposition (SVD). Parameter identification of five-storey shear-type is simulated with incomplete output. The results show that the identified parameters have high precision, and the proposed method is effective and robust on noise.