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In order to estimate the optimal standard deviation which is the desired output of the neural network, the following stages will be undertaken.
Thus, determining optimal standard deviation involves a trade-off between the level of quality based on functional performance and the costs associated with the standard deviation.
The segment D illustrated in red is the tangent to the curve at the index σopt where the derivative is null P′(σopt) = 0 and D is defined by the following equation: Figure 4 PSNR results and normalized cross-correlation used to determine the optimal standard deviation.
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The results indicate that the economic sensitivity is often higher than the energetic sensitivity for most of the optimal solutions, with standard deviation accounting up to 7% of its mean value for the SPB, whereas that percentage is always under 3% for the TPES.
In Figure 8, we plot optimal CWmin against standard deviation of vehicle speed.
We show that compared to state of the art CSG algorithms, DCCF outperforms them by many orders of magnitude, providing solutions whose average quality nears 98% of the optimal (with a standard deviation of 1.4%) in all tested cases.
The quantitative range was determined from the range of ten-fold mixed plasmid dilutions that gave optimal linearity, efficiency, standard deviation and coefficient of variation between replicates.
The Area Under Curve validation statistic did not provide a meaningful optimal multiplier for the standard deviation.
In order to select the optimal process, setting the standard deviation of tablet tensile strength and tablet weight for each tablet batch was considered.
The optimal value of the standard deviation is manually adjusted around the computed value in the range of [-2Δσ [-2Δσ.
Selecting the optimal value of the standard deviation in a Gaussian filter depending on few properties of the signal knowledge is proposed in [5].
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