A secular variation model valid for 2010.0 2015.0, based on the extrapolation through exponential smoothing of observatory monthly mean values, is proposed as a predictive secular variation model.
A non-dimensional pressure fluctuation intensity, which indicates the relative dispersion to its mean value, was proposed to gauge the uniformity of the contact pressure distribution, similar to the definition of the turbulence intensity in fluid mechanics.
Next, a practical real-time supervisory Energy Management strategy basing on Mean Values (EMMV) is proposed.
In this article also, we assumed the state and connection lifetimes to follow exponential distributions with different mean values, for illustrating the proposed approach.
In the comparison of PSNR, the mean value of the proposed method is about 0.61 dB higher than that of the other three.
In the comparison of SSIM, the mean value of the proposed method is about 0.013 higher than that of the other three.
For the Bullinger, the mean value of the proposed method is 0.5 dB lower than that of MRF, and the maximum value is only 0.22 dB lower than that of MRF; but the proposed method has a good visual appearance.
According to mean value theorem, the proposed threshold criteria (e.g., maximal allowable treatment capacity) in ICSs could be determined by the method of a constant-slope isocline family, directly indicating treatment limits at various nutrient media in biostimulation.
This method involves an inflation step where the bounds of the remainder term for the kth-order Taylor expansion of the model ODEs are inflated by 1 ± α. Evaluation of the Taylor expansion is performed using the Extended Mean Value (EMV) algorithm proposed by Rihm [ 19] using mean value forms [ 20] and matrix preconditioning.
Thus, in our current analytical method, we analysed the data of each processed day independently by proposing mean values and prediction intervals for each particular processing day.
