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The basic idea is to eliminate the estimation bias by adding a correction term in the least squares (LS) estimates, a set of stable digital prefilters are suitably designed to preprocess the input sampled data from multi-input channels for the purpose of getting the bias term arisen by colored noises in LS estimates, and further to derive a bias compensation based RLS algorithm.
The proposed logic based scheme estimates a set point that prevents wheel-slip and provides a suitable control to maintain the system at the prescribed set point.
Instead of estimating the SD model directly, the MLLR method estimates a set of linear transformations to transform an SI model into a new SA model.
MLLR estimates a set of transformations that can be shared by several model components, hence reducing the amount of adaptation data required [41, 42].
The maximum likelihood linear regression (MLLR) method[2] estimates a set of linear transformations to transform an SI model into an SA model.
Camera Registration and Point Triangulation: The SfM algorithm estimates a set of camera parameters, such as the focal length, rotation matrix, and translation vector, for each image and triangulates 3D positions of feature points observed in each image.
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This interacting dynamic model allows to appropriately estimate a set of data association hypotheses that are used for the estimation of the object trajectories.
To reduce the effects of force model error on precise orbit determination, one can estimate a set of empirical parameters.
Inverse modeling of the Hg retraction datasets enables us to estimate a set of primary and secondary IAFs.
We next estimate a set of econometric models to investigate the statistical significance of the previous evidence.
First, a method of experimental design is applied to estimating a set of parameters defining the boundary shape to be identified.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com