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In parameter design experiments, it is common to fit a regression model to the original response and then use the model variance induced by the noise variation to determine robust settings of the control factors.
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We obtain some general characterization of the robust settings in terms of the absolute slopes in the control-by-noise interaction plots.
Users can control who is able to see the content on their timeline through a robust set of privacy settings.
The simplicity and graphical appeal of the latter conditions provide further insight into the choice of robust settings in these experiments.
The variance of the distribution is larger for the more robust settings, which is also to be expected.
The proposed computational strategy is robust to the settings of the initial configurations, even if the topology optimization problems have multimodal distributions of solutions that include many local minima with insufficient performance, and stably improves the regularized fitness value.
This indicates that our group clustering approach is robust for different settings of the cluster parameters.
The results of five methods are compared in Table 1 and Figure 2. Since the results are robust for different parameter settings of (p1, p2), the results in Table 1 are the mean values in all datasets.
Results shows that the proposed method remains accurate and robust in parameter estimation under different settings of synthetic networks and outperforms other selected approaches in predicting the status of network formation.
In addition, based on the robust plating settings, the composition of Sn Zn alloys could be precisely controlled and predicted by adjusting the composition of the plating baths.
This is achieved by using robust optimization techniques to determine the optimal process settings of adjustable tools in the final roll forming stand.
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