The geometric and kinematic design of the robot was performed based on graphical modeling and simulation through interference analysis and visualization.
The performance of the network in the prediction of microbial counts was based on graphical plots and statistical indices (bias and accuracy factors, standard error of prediction, mean relative and mean absolute percentage residuals).
The second approach was selected based on graphical comparison and its plausibility, particularly at the butt log.
The proposed methodology is based on graphical techniques and uses the concept of stage composition lines in the transformed composition space.
This article therefore presents a new methodology for developing educational games based on graphical notations and divided in six phases: design of chapters, design of scenes (scenarios, characters, actions and dialogues), design of educational challenges in the game, design of the adaptation, design of the emotional experience and design of collaboration.
Gaussian distribution was found based on graphical plots and the Kolmogorov-Smirnov test for normality; thus, parametric methods were used to analyze data and all data were presented as means and standard deviations (SD).
Finally, we compared GENIE3 to three existing approaches based on the computation of mutual information (MI), namely CLR [11], ARACNE [12] and MRNET [14], and to one approach based on graphical Gaussian models (GGMs) [20].
The fit of all regression models based on graphical assessment of residuals and BLUPs was considered adequate.
Process integration approach based on graphical pinch methodology for targeting and water network synthesis is often employed.
Comparisons based on graphical representations are made between numerical and exact solutions.
