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An analysis NURBS surface separately created with the foregoing NURBS surface is utilized to exactly describe geometry and approximately solve unknown solutions in finite element analysis (FEA) based on the IGA associated with a generalized shear deformation theory (GSDT).
From Equations 2 to 4 and 9 and 10, we can solve unknown parameters τ i and p i, f numerically with a given set of frame size (FS1, FS2....FSK) and BERs (p1, b, p2, b....p K, b ) corresponding to the K hosts.
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This model is trained for solving unknown coefficients (beta _j).
We show that enrichment-corrected deconvolution of two mixture datasets returns the best approximation of known mixture proportions (Figs. 4 and 5), demonstrating suitability for solving unknown mixtures of known components.
Based on the Lipschitz or norm-boundedness assumption of the unknown nonlinearities, the mean-value theorem is utilized to solve the unknown time-delay problem.
Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem.
In the design, adaptive compensation technique is employed to solve the unknown information problems of the dead-zone slopes and breakpoints, and the neural networks are used to identify the unknown uncertain nonlinearities.
The key of this method is to solve the unknown delay from an implicit equation.
In applying SRSM, the most important task is to solve the unknown coefficient a i.
An improved genetic algorithm is applied to solve the unknown parameters of the model.
In some trials, the data from the1H-1H COSY, TOCSY, NOESY and ROESY experiments were not required to solve the unknown.
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