Scales should be one-dimensional in order to avoid ambiguity and measurement errors.
In Optimized spline [7], by taking advantages of calculus of variations, the optimization problem is simplified from a nonlinear infinite dimensional case to a linear finite dimensional one in order to design compact-support interpolation kernels for a class of signals.
They are usually produced to strict dimensional tolerances in order to assure efficient performance in the highly demanding operations of automated consumer-products industries.
Furthermore, we have also demonstrated the realization of the dimensional tailoring in order to make the strong forward scattering shift to the desired wavelength.
This study concentrates on the optimization of two dimensional cavitators in order to decrease drag coefficient for a specified after body length and velocity in a potential flow.
Self-organising maps (SOM) [20] of the feature vectors associated with each bread image were useful to represent them in a lower dimensional view, in order to better understand the meaning of their respective MFS.
Next-generation, high track-density linear tape drives will require improved tape guiding and dimensional stability in order to achieve better performance and higher storage capacities.
Thus, size effect theories, based on a multiscale approach, analyse the intrinsic (due to microstructural constraints, e.g., grain size) and extrinsic effects (caused by dimensional constraints), in order to improve the knowledge in materials science and applied mechanics.
But engineering practice favors reduced dimensional models, in order to describe deformation processes in its most natural way by surface- and line-like geometries, mainly for ease of error controling.
We have thus used three dimensional graphics in order to locate the region of maximum C2+ yield as a function of catalyst bed hot spot temperature and linear space velocity.
The radial basis function kernel is used to transform the data into a higher dimensional space in order to be able to perform the separation in the non-linear region, while Naïve Bayes classifier is based on the Bayes' theorem with independence assumptions between predictors.
