Research on Anti-noise Processing Method of Production Signal Based on Ensemble Empirical Mode Decomposition(EEMD)

The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition(EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions(IMF) from high frequency to low frequency by using the Empirical Mode Decomposition(EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion(CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.

Boundedness in a chemotaxis system with nonlinear signal production

This work deals with the zero-Neumann boundary problem to a fully parabolic chemotaxis system with a nonlinear signal production function f（s） fulfilling 0 〈 f（s） 〈 Ksα for all s 〉 0, where K and α are positive parameters. It is shown that whenever 0 〈 α 〈 2/n （where n denotes the spatial dimension） and under suitable assumptions on the initial data, this problem admits a unique global classical solution that is uniformly-in-time bounded in any spatial dimension. The proof is based on some a priori estimate techniques.

Blow-Up and Boundedness in Quasilinear Parabolic-Elliptic Chemotaxis System with Nonlinear Signal Production

In this paper,we consider the quasilinear chemotaxis system of parabolicelliptic type■.under homogeneous Neumann boundary conditions in a smooth bounded domain■n≥1.The nonlinear diffusivity D(ζ)and chemosensitivity f(ζ)as well as nonlinear signal production g(ζ)are supposed to extend the prototypes■We proved that if m+k+l>1+2/n,then there exists nonnegative radially symmetric initial data uo such that the corresponding solutions blow up in finite time.However,the system admits a global bounded classical solution for arbitrary initial datum when m+k+l<1+2/n.

Global Dynamics of a Quasilinear Chemotaxis System with Indirect Signal Production and Generalized Logistic Source

This paper considers the following quasilinear chemo-taxis system with indirect signal production and generalized logistic source{u_(t)=▽·(▽(u)▽u)-▽·(S(u)▽v)+μ(u-u^(Y)),x∈Ω,t>0 O=△v-v+w,x∈Ω,t>0 w_(t)=-δw+u,x∈Ω,t>0 under homogeneous Neumann boundary conditions in a smooth bounded domainΩ■R^(n)(n≥1),where the parametersμ,δ>0 andγ>1,the functions D(u)and S(u)are supposed to be smooth fulfilling D(u)≥C_(D)(u+1)^(-a)and S(u)≤C_(S)u(u+1)^(β-1)for all u≥0 with C D,α,β∈R and.It is proved that the corresponding initial-boundary value problem possesses a global bounded classical solution ifα+2β<γ-1.Moreover,ifμis suitably large,the asymptotic behavior and convergence rates are also been considered.