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This chapter focuses on data warehouse data model tuning techniques.
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The contributions of the paper lie in the first specification and application of recent (i) model approximation and (ii) H∞ structured controller tuning techniques on a complex aeroelastical aircraft model, used by engineers to design control strategies.
For an application represented by a directed acyclic graph, we present a model where voltage scaling techniques for processors can be combined with frequency tuning techniques for NoC links to save overall system energy consumption.
The supervisedly tuned techniques are local MDS (Venna and Kaski, 2006) and neighbour retrieval visualiser (NeRV) (Venna et al., 2010).
Using a model-based tuning technique, an optimal proportional-differential (PD) controller is designed based on the previous calibration.
Experimental measurements show that the dynamic response of the TDVA with friction can be accurately predicted and the time-delayed control can be precisely achieved by using the modelling and tuning technique provided in this paper.
The comparisons of numerical simulations with the experimental results were used to verify the validity of mathematical modeling on VEH with multiple nonlinear tuning techniques.
2, while Sect. 3 presents the general model, Sect. 4 describes the specific proposed model, the Kalman filtering technique and the heuristics used for model tuning, Sect. 5 describes the proposed experiment and results.
As a consequence of the tuning of the FSS, for some tuning techniques a rotation of the polarization of the emitted light is observed.
Modifying the sequence, number, or position of operator sites within a promoter are common tuning techniques in synthetic biology.
This article only covers the basic tuning techniques.
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