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Therefore, in the following analysis, an HPF was tuned based on the Akaike information criterion (AIC).
The parameters of a finite element model with smeared crack material model was tuned based on the stress-strain relationship of LSHCC measured from the tensile tests in this study.
The parameter in (11) was tuned based on the PW79 dataset.
Five-fold cross-validation was performed and the SVM margin softness parameter (C ∈ { 2 − 3, 2 − 2, …, 2 6, 2 7 } ) was tuned based on the training accuracy.
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This would directly answer the question of whether the SAT setting is likely to be tuned based on the experimental data, regardless of whether the setting is the optimal one or not.
Reference images were tuned based on consumer preference expectations.
Some of the parameters, e.g., language model weights, were tuned based on the WERs of dev.
The weights can be tuned based on the specific security goal to be achieved.
In addition, empirical models were tuned based on dataset collected in situ.
The parameters associated with the algorithm are tuned based on the Taguchi method.
The controller performance can be tuned based on a reference model.
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