Five repeat analyses per sample were performed using FI/MS and the (C) and (G) content determined by utilizing the matrix-matched (C) and (G) ANN models.
Sequentially, the functions and components of the pricing models are discretized by utilizing the operational matrix of fractional integration of Legendre wavelets.
By utilizing the propagator matrix method, exact closed-form solutions of multilayered 1D hexagonal PQC nanoplates are then obtained by assuming that the layer interfaces are perfectly contacted.
Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H∞ "noise" attenuation level.
Therefore, ( {overline{mathbf{F}}}^k ) and R k are designed step-by-step, which starts by computing the transmit space matrix and the receive processing matrix of the first user, then computes the matrices for the second user by utilizing the receive processing matrix of the first user and so on.
By utilizing the free weighting matrix technique, a less conservative delay-dependent synchronization criterion is derived.
In this research, considering the effects of drawbar on the dynamic behavior of the milling motorized spindle-bearing system, a double-rotor model of spindle-drawbar-bearing assembly has been established by utilizing the whole transfer matrix method (WTMM) and a nonlinear rolling bearing dynamic model including the centrifugal force and gyroscopic effects.
Spectral clustering-based (SC) method [ 13] converts the problem to a quadratic optimization with constraints by utilizing the methodology of matrix analysis, which is generally applied to the fields of image segmentation and complex network clustering.
Neighbor Joining [ 19, 35], on the other hand, although it constructs the tree in a step-wise fashion by utilizing the input distance matrix and by joining the closest neighbors, is more popular in phylogenetic analysis as it can be used on very large data sets for which other methods (e.g. minimum evolution, maximum parsimony, maximum likelihood) are computationally prohibitive.
The weight matrix of DDRNN-CMTPAF is constructed by utilizing the unitary signal space matrix obtained from singular value decomposition for the receiving signal matrix.
