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The initial digital model for each patient was then virtually modified by two different modelling techniques to achieve the corresponding target model: parametric and freeform modelling with the software Geomagic®.
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There are two main categories for such models: parametric and non-parametric models.
Approximation-based control technique is employed to handle the model parametric uncertainties and unknown disturbances for the follower.
This paper presents the detailed modelling, parametric studies, and optimizations for two recently proposed magnetic negative stiffness dampers (MNSDs).
This includes parametric models (parametric planning model and parametric packet-layer model) and bitstream layer model.
In this paper the performances of seven models (five parametric and two statistical), two of these original, were analyzed.
These models are parametric and can be used for different cases of molecular transport.
In our study, we compared two ways of incorporating GBS data into models: parametric method (GBLUP) and semi-parametric method (RKHS).
So, the main advantage of proposed method is simplicity in mathematical computations and modeling parametric uncertainties.
The mean number of recurrences can be modelled using semi-parametric Cox models and parametric models [ 17, 39].
Then, based on the stochastic mixture model framework, we review parametric and non-parametric approaches for underlying stochastic source assignment.
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