Sentence examples for quantified simulation results from inspiring English sources

We use the quantified simulation results to evaluate alternative software architectural designs.

Solid curves show evaluations of (23) and markers quantify simulation results.

While the limitations and errors in using GPR data as observations for inverse multiphase flow simulations are frustrating and difficult to quantify, simulation results indicate that the error and bias in the PCE saturation values are small enough to still provide reasonable optimal permeability values.

Sensitivity analysis is a useful tool to identify key model parameters as well as to quantify simulation errors resulting from parameter uncertainty.

Hydrodynamic forces acting on the beneficiated particles are also quantified from the simulation results.

It is also shown that these sensitivities are valuable for ranking the parameters from most to least influential, designing improved experiments, and quantifying uncertainty in the simulation results.

Simulation results quantify the relative throughput delay performance penalty of the different security policy options, and can be used for designing security policy configurations that strike the desired balance between security and performance.

The simulation results quantify the effects of non-uniform stored energy distribution and orientation gradients present in the cold-worked microstructure on recovery by subgrain growth, and hence on the spatial distribution of nuclei and their orientations.

The proposed schemes, namely the pilot scheduling schemes based on performance degradation and user grouping, are discussed in Section 4. Our simulation results quantifying the benefits of our proposals are presented in Section 5, while the conclusions follow in Section 6. Notation: ({mathbb {C}}) indicates the set of complex numbers.

The KS criterion allows us to quantify the similarity between the simulation results and a given theoretical distribution.

Using analytical and simulation results, we quantify the dependence of optimal coarse and fine bin selection on system parameters such as (1) size of FFT used in scanning; (2) probability of detection and false alarm of the underlying sensing algorithm; (3) signal-to-noise ratio of the received signal, and (4) expected number of available channels.