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To capture the non-stationary distribution of DT, we utilize the Gaussian process latent variable model for dimensional reduction.
This is used as a model for dimensional competition in propagation effects in the presence of one-dimensional subsystems on a surface.
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In this paper a Buckingham Pi model (based upon Taguchi macro-model) for dimensional accuracy (Δd) is presented aiming at such generalisation.
Then the effect on the predicted stress using two different empirical models for dimensional change is studied.
Finally, in our calculation of NSP, we assume that the life cycle models for dimensional lumber, OSB, and magazine paper are similar on the CNNF as on non-federal forests [9, 10].
Finally, in our calculation of NSP, we assume that the life cycle models for dimensional lumber, OSB, and magazine paper are similar on the CNNF as on non-federal forests [ 9, 10].
A simple model system for dimensional competition in nanostructures has been proposed.
An improved model for predicting dimensional errors in turning process is presented.
However, it is computationally inefficient to build an accuracy RF model for high dimensional data.
The SEM framework forms the basis for a proper use of the most prominent one- and two-dimensional decompositions and provides an argument for using the bivariate multiple regression model for two-dimensional decomposition.
The authors proposed a data-recording model for two-dimensional (2D) ISAR imaging of rotating parts.
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