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Given a text, the speech is synthesized by interpolating the parameters computed by ψ∗ and ϕ T with weights r∗and 1−r∗.
When TΔ S ° = 0, Δ G ° can be envisaged to be solely contributed by Δ H °. We determined the temperatures at which Δ H ° is equal to Δ G ° (by interpolating the parameters described above from the corresponding Δ C p ° plots), and they were found to be 288 and 329 K for the binding of SAHA and C6-cSAHA, respectively.
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The LPV nature of the system is then retrieved by interpolating the value of the parameters at other operating points for example with a polynomial function which is fitted through the parameters identified at the operating points considered.
The parameter β, computed by interpolating the empirical curves (see Eq. (1)), defines the average time scale of individual daily mobility and it is a characteristic of each city.
In the online phase of this approach, a linear ROM is constructed in real-time at a queried but unsampled parameter point by interpolating the pre-computed linear reduced operators on matrix manifolds and therefore computing an interpolated linear ROM.
Therefore, the protocol described above was extended by not only determining parameter trajectories from phenotype A to phenotype B, but also backwards from phenotype B to phenotype A. A backward trajectory is obtained by interpolating the data from phenotype B to phenotype A, whilst reoptimizing the parameters.
A linear parameter varying model is then constructed by interpolating the region between the neighbor frozen operating times.
An empirical model with a reduced set of parameters was successfully developed and validated in the area by interpolating the results from the dynamic factor analysis across the spatial domain (coefficient of efficiency across the domain: 0.66 0.99).
Parameter trajectories describing the phenotype transition were determined by interpolating the data between phenotypes.
The interpolating gain-scheduled controller is obtained by interpolating the state space model of the linear time-invariant (LTI) controllers estimated for fixed values of the scheduling parameters and a linear least squares problem can be solved.
However, once having calibrated the model for some Reynolds numbers, the optimal parameters may be extrapolated for any other Reynolds number within the range of optimization, by interpolating the correlation variables with polynomial functions.
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