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Generic modeling parameters are established for the STM procedure, enabling the models to be adapted to new repair applications.
In addition, sensitivity study on key modeling parameters are conducted.
Design and modeling parameters are obtained via regression from four sets of experimental data.
Given the gear sizes, the transmission path effect modeling parameters are estimated.
The technology dependent MOSFET modeling parameters are extracted from characterization measurements, datasheets and PSpice simulations at various temperatures.
The hydraulic fracturing characteristics attributed to the modeling parameters are investigated through comparisons of the simulation results.
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Frequency domain measurements with further tuning of numerical modeling parameters were used to extract circuit parameters.
Subsequently, significant differences in pore size distributions are observed depending on which modeling parameters were used.
With this goal in mind, a two-steps learning algorithm using Swarm Intelligence for optimizing the modeling parameters is introduced.
The influences of various modeling parameters were assessed using analysis of variance (ANOVA) and design-of-experiments (DOE) principles to perform screening of 12 structural parameters.
As a result a unique set of modeling parameters was obtained for the whole precipitation process and range of temperatures considered.
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