Both methods of mix integer linear programming (MILP) control of the heat pumps lead to a much improved, almost flat electricity consumption profile.
To meet assumptions of normality, linearity, and homoscedasticity essential to methods of linear regression modeling, we logarithmically transformed IsoAsp, GAG, and protein values and used the D'Agostino and Pearson omnibus normality test to confirm a normal distribution.
We introduce a new reuse mechanism called matrix shape which enables us to derive the implementation of both the sequential and the parallel version of the iterative methods of Linear Algebra Kernels (LAKe).
In this chapter, we introduce the basic concepts and solution methods of linear systems.
From these intersections, combined properties closures have been constructed using conventional methods of linear programming.
In this paper, using the methods of linear algebra and matrix theory, we discuss the admissibility of linear estimators in model (1.1) under the matrix loss (1.2).
Such treatment makes it possible to apply the well-developed methods of linear functional analysis to (1.5) with a linear and bounded operator.
Meanwhile, traditional methods of linear channel estimation already attained optimal estimation performance such as utilization efficiency, so it is difficult to improve them further.
The reaction conditions discussed in this report relate to the methods of linear scanning, cyclic voltammetry and constant current oxidation.
Many well-developed concepts and analytic methods of linear time-invariant (LTI) systems cannot be applied to LTV systems.
By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set.
