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Part II provides recommended empirical input data and example results generated from the model.
The dynamic risk profile generated from the model is useful in operational decision making to prevent accidents and enhance the safety of drilling operations.
Validation of the procedure is presented in which a time series generated from the model is Fourier transformed for comparison with the target spectral density.
An optimization scheme is adopted to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions.
This paper shows how a diagnostic system satisfying these requirements can be automatically generated from the model based simulation used to produce an automated Failure Modes and Effect Analysis (FMEA).
The optimal conditions generated from the model for the targeted 40% palmitic acid incorporation at the sn-2 position were 3 mol/mol, 14.4 h, and 55°C; and 2.8 mol/mol, 19.6 h, and 55°C for substrate ratio (moles of total fatty acid/moles of tripalmitin), time, and temperature, respectively.
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To avoid possible interference of the code generation process with the results, we used three different programs versions generated from the models, on which the mutation testing was applied to assess the generated test sets.
The purpose of our comparison is to evaluate the effectiveness of each generation strategy measured by the capacity of distinguishing mutants of the programs generated from the models.
Optimal conditions generated from the models were determined as 60 °C; 8 h, 4 mol/mol for temperature, time and substrate molar ratio, respectively.
The results of numerical experiments demonstrate that the algorithm accurately estimated key parameters associated with stationary target functions generated from the models themselves.
However, as there are no tools available to work directly on embedded system models, our alternative was to use the C code generated from the models.
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