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In this work, a data-driven algorithm is proposed to directly design a gain-scheduled model-reference controller without need of identifying and transforming a model of the process.
When we last left off, Omote (real name: Nobumichi Asai) was transforming a model's face using projection-mapped makeup.
Transforming a simulation model from DEVS representation to SMP2 representation is of great significance for model reuse.
The maximum likelihood linear regression (MLLR) method[2] estimates a set of linear transformations to transform an SI model into an SA model.
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.
In a step of such a workflow, an event may be designed to transform a given model with a visually specified graph transformation rule.
Our experimental results indicate that our system can transform a 3D model plausibly.
To do the same, you don't have to redesign any parts of an organization, reengineer a work process, or transform a business model.
In this paper we present the means to transform a formal model into different application domain oriented views (scenario views).
A logarithmic transformation of the data may be appropriate because it transforms a multiplicative model into an additive model, and thus relative differences into absolute differences through the equation log(A/ B) = log(A)- log(B).
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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