Interdependent cross-disciplinary design parameters are separated in accordance with their uncertainty levels, and different fidelity analysis models are assigned to each set of parameters accordingly.
This is mainly because the geomorphological information parameters are separated in the legend, and that explanatory information is gained from combinations of them.
The setup of the algorithm control parameters is separated and suboptimized with the segmentation and the tracker screened one at a time.
As shown in Figure 4, the distributions of mean (p′ j,λ) for random data and coded data with correctly estimated coding parameters are separated.
However, this problem may be remedied by applying the discrete Fourier transform to (2) to extract the delays and form an equivalent equation in which the unknown parameters are separated in individual terms: X m ( f ) = ∑ n = 1 N α ( ρ m, n ) exp - j 2 π n f f τ m, n S n ( f ) + ∑ n = 1 N ∑ p = 1 P m, n γ m, n, p exp - j 2 π n f f τ ^ m, n, p S n ( f ) + ξ m ( f ) (3).
It is important to note that although the structural uniqueness of a CRN definitely reduces the degree of non-identifiability (since zero and non-zero parameters are separated), it does not necessarily imply structural identifiability [ 53], and this latter property has to be checked by further numerical methods [ 31, 32].
Then, analysis of variance (ANOVA) was computed using SPSS version 20, and mean values of all the parameters and the standard errors of each parameter were separated using LSD at 5%% level of significance.
The S parameter was separated in different layers, and the density/type of vacancies was analyzed at different positions in the film.
Where there were significant differences in the measured compost parameter, the treatment means were separated using LSD or standard error of difference (SED) (p < 0.05).
