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An artificial neural network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and heat treatment parameters of low alloy steels.
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Fifty thousand simulations of the correlation between two random vectors of 14 elements were performed.
Monte Carlo simulation of the proposed correlation showed a gamma distribution for choke flow rate Q, with the most probable value of 4,120 stb/day.
Models and software products have been developed for simulation and prediction of the correlation between processing parameters and properties, time temperature transformation diagrams, and fatigue stress life (S N) diagrams, based on trained artificial neural networks.
This simulation studies the correlation of vehicle parameters and the influence of different psychological thresholds of factors on the EV purchasers' willingness.
Such simulations would helpfully allow the strength of the correlation between correlated variables and the amount of confounding amplification existing between the two models to be varied.
Top panels: four examples of simulations of the pure-correlation model having different values of a vestibular ranging from 0.01 to 0.12.
An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated.
Another difference to the simulations is the correlation of the MIMO channel, which was approximately in the measurements.
Analytical examination and simple mathematical simulation of the experimental results found good correlations between the size of the deformed area after scratch and curing operating parameters.
LDFC performed all experimental simulations and analyses, except the determination of the correlation statistical tests, performed by CCH.
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