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Based on reference models and knowledge modelling, this method requires the definition of a collaborative information system to assist design actors.
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Moreover we show how previous work on modelling this particular method allows to obtain analytically the parameters of the Markov chain.
Both uniform flow and logarithmic flow with wind twist can be modelled using this method.
Bruni et al. [32, 33] construct the hardware-in-the-loop hybrid pantograph platform, in which the catenary is modeled by this method.
Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps.
Comparing to the previous method base on parametric fan model, this method has advantages of better accuracy, stronger robustness and higher efficiency under various conditions of fan characteristics and disturbances.
In relation to the system model, this method allows estimation of its parameters.
When combined with 4-gram de-lexicalized language model, this method achieved an accuracy of 80.1% on binary pitch accent classification and 89.6% on boundary detection.
Even though it restricts the problem model, this method is advantageous in that it finds an optimal solution in a reasonable computation time.
Similar to the single-gene model, this method can be extended to deal with multiple sequences in a phylogenetic context and to test hypotheses concerning variable selection pressures among lineages and sites [40] [42].
Compared with the method based on the logit model, this method has a higher sensitivity (+14%) and slightly weaker specificity (-3%).
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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