Sentence examples for model the output from inspiring English sources

To this end, we model the output measurement noise using random variables with heavy-tailed probability density functions (pdfs), focusing on the Laplacian and the Student's t distributions.

We give an algorithm to detect local approximate symmetries in a discrete point set derived from a B-rep model: the output comprises the model's potential local symmetries at various automatically detected tolerance levels.

Even though the composition of these pixels for different classes can be estimated with a pixel un-mixing model, the output provides no indication of how such classes are distributed spatially within these pixels.

Parametric survey reveals that, for a fixed activation overpotential model, the output voltage can be improved by increasing the pore size of anode, while the cross-sectional geometry has smaller effect on the cell performance.

The objective of this work is to model the output responses namely; surface roughness (Ra), cutting force (Fc), cutting power (Pc), specific cutting force (Ks) and metal removal rate (MRR), during the face milling of the austenitic stainless steel X2CrNi18-9 with coated carbide inserts (GC4040).

In the Woods Saxon SR (WSSR) model, the output signal-to-noise ratio (SNR) can be optimized just by tuning the WS potential's parameters, which delivers the most significant merit that the limitation of small parameter requirement of the classical bistable SR can be overcome, and thus a wide range of driving frequencies can be detected via the SR model.

Through the patients' central role in the CAG-model, the output exceeded the initial expectations.

The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output.

In the model structure, the output is calculated in Eqs.

Step 4: Calculation of the statistical properties of model outputs: The model outputs are estimated followed by the estimation coefficients.

GSA allocates the uncertainty in the model output to the various sources of uncertainty, namely the model parameters.