Based on the above network model and assumptions, our objective is to derive an optimal association scheduling method that maximizes the throughput of a target vehicle.
Therefore, this article suggests the coverage method that adds the obstacle avoidance method to the constrained coverage method that maximizes coverage while maintaining the given connectivity [8, 15].
This analysis is a multivariate method that maximizes shared structure among multiple datasets drawn from the same samples.
A fundamental and widely applicable parameter estimation method is Maximum Likelihood (ML) method that maximizes the likelihood of the observed data with respect to parameters.
SCCA is a two-block method that maximizes the correlation between independent 𝒳 and response variable 𝒴.
This classifier can serve as a proxy for any method that maximizes the likelihood of unlinked markers.
Here, we explore the characteristic of modularity density D, and derive a new spectral clustering based method for maximize D (Dλ) (SpeMD).
Here, the lower and the upper admissible bounds for all fluxes, which have neither been measured nor have associated knockouts, are obtained through the application of an optimization-based method that maximizes/minimizes the flux of interest under the same set of constraints.
Figure 2 A split based on a question that maximizes the increase in log likelihood.
The method is based on a simulated annealing procedure that maximizes the proportion of genetic variance that can be explained by differences between groups of populations, assessed with the among group genetic variation (Φ CT) coefficient as estimated by AMOVA [ 118].
