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Whereby the optimization of the number of sensors and their location is of the utmost interest.
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For each specific design vector, an interval analysis method is applied to calculate the interval of the objective function caused by uncertainty, and whereby the optimization nesting problem can be solved.
The optimization of both variables though is computationally intensive.
Currently, the optimization of the experimental design is of interest.
The optimization of CRT programming involves a variety of techniques.
Moreover, we also report the optimization of the sequencing reagents.
The proposed MIPPM is used to predict the intervals of the constraints, and whereby eliminate the optimization nesting.
In OK, the weighing coefficient λ i can be calculated by solving an optimization problem, whereby, the variance of errors will be minimized, subjected to unbiased condition.
Optimization was achieved using 32 full factorial design whereby the effect of HPC (X1) and EC (X2) concentrations on mucoadhesive strength (Y1), swelling (Y2) and in vitro drug release (Y3) profile were analyzed.
The conditions at failure are defined by employing a critical plane approach, whereby the orientation of the localization plane is specified by solving a constrained optimization problem.
From the optimization point of view, this corresponds to a mixed integer dynamic optimization (MIDO) problem.
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