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Model 1 included the three current prognostic factors and was later referred to as the reference model.
Arbitrary values were predefined for the independent association between these predictors and their respective outcome, with b0 = −3.43, b1 = 1.45 and b2 = 1.18 (where we chose x 1, x 2 ∼ N 0, 1 and ρ x 1, x 2 = 0, i.e. x1 and x2 are not correlated) which we further refer to as the reference model.
We first propose to use an ideal solution, which is in fact an optimization solution of fault detection filter achieved on the assumption of the system model is perfectly known (i.e. without modelling errors), as the reference model of robust fault detection filter design for uncertain systems.
Obviously, models generated by the same modelling technique as the reference model generally performed best, reflecting a "home advantage" over models generated by a different modelling technique than the reference model.
The models generated with the same modelling technique as the reference model showed the best performance, except when the reference model was generated with CART, in which case the LR model had the best performance (AUC 0.712, Table 3).
We will refer to this known core field model as the reference model.
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In order to compare the fit of crowns to abutments that served as the reference models, the Geomagic Qualify 12 software for structured-light 3D scanning and modelling was used.
The RLS-4 and RLS-5 head models used as the reference models in this study include a detailed, subject-specific geometry of the whole head and include all major tissue types.
AS and RA defined the reference model and obtained the numerical parameters used in the paper.
Figure 5 shows that the fraction of busy time sensed by each transmitting station is higher in proposed model as compared to the reference model [1].
As a simplification of the reference model, the present model is composed of only five subdomains.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com