Exact(8)
In this algorithm, we first prepare a group of virtual samples with random samplings from a certain initial range for each parameter to estimate.
Tests have been performed with virtual samples (n = 64).
The compressive strengths of the virtual samples are also evaluated using the FE analysis.
Frequency distribution of TGS calculated from the virtual samples is depicted in Fig. 2.
Virtual samples were generated by VCCTL, using particle size distribution and phase composition as input.
For spectroscopy, atomic absorption spectra can be practiced by moving virtual samples into a flame[17].
Here, a set of virtual samples with different number of anisotropic insulations are generated.
Problem of data scarcity is addressed analytically and virtual samples are produced via enthalpy formulation for battery heat generation.
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