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Robust sampling designs for compartmental models under large prior eigenvalue uncertainties.
In addition, we validate the accuracy of our models under large head pose variation and facial occlusions in the Head Pose Image Database (HPID) [3] and Caltech Occluded Faces in the Wild (COFW) datasets [4], respectively.
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A separate parametric study was conducted to calibrate the number of grid beams and the stiffness of diagonals to produce acceptable agreement with the "exact" model under large deformations.
Furthermore, a large misalignment angle model and a dimensionality reduction Cubature Kalman Filter using measurement augmentation (MACKF) are also employed to solve the invalidity of the linear model under the large misalignment angle.
As an alternative to continuum approaches, the discrete element method (DEM) has been used by researchers to model granular material under large deformation.
The conventional models are usually developed under large temperature difference, and the fully liquid-filled downcomer are taken for granted.
The proposed model is investigated numerically under large amplitude oscillatory shear deformation.
The present study is concerned with the numerical homogenization of hyperelastic two-dimensional model foams with irregular microstructure under large macroscopic deformation.
Consequently, a calculation model for the Nu of CARCC under large eccentric compression was proposed.
Both of the models exhibit large plastic deformations under dynamic loading.
P-values greater than 0.05 indicate that the model is adequate to the data, although the χ 2 test over-reject true models for large samples or under non-normality [49].
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