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The resulting adaptive hybrid interval type-2 FNN control system shows better performance than the adaptive hybrid type-1 FNN control system; it reduces both the tracking error and the control effort and it is more flexible in the design process.
At day time the system shows better performance with respect to the average profile of 34,1%, while at night its performance decays drastically, due to the dim lighting of the area.
By setting the initial decoded Wyner-Ziv frame as the side information and refining the side information progressively, the proposed system shows better performance than the conventional DVC systems at low bit rates.
As shown in Figures 7 and 8, while the size of all feedback for RBF with MESC (2 bit CQI) is 6 and 4 bits smaller than that of SUS-ee with MESC, respectively, the proposed system shows better throughput performance.
It is also evident from all the graphs of Fig. 7 that the confidence interval of our proposed TCDC system is much lower compared to that of the DGA and ACDA systems, i.e., the TCDC system shows better performance stability compared to them.
Though diagonally braced system shows better results than that of the other two systems, practically, it is difficult to implement as it would hinder the movement of users around the space and thus central bracing system is more effective for soft story frames.
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In a comparison of gene transduction efficiency, the LpDNA system showed better cell transduction than the pDNA system.
A first test is conducted on a nonlinear smooth 2 degree-of-freedom spring mass system, showing better convergence of the HBM.
The new system showed better output compared to the previous attempts, confirming its applicability for accurate measurement of real time wheel forces.
After incorporating the two variables, θ1 and θ2, into the original Fitts' model, the extended Fitts' model with 3D target arrangements for spherical coordinate system showed better agreement with the empirical data than previous models in terms of the correlation coefficient and the standard error of the residuals for the measured and predicted MTs.
Among the different forecasting systems, the fuzzy system showed better performance in terms of lower numbers of false positives for all validation datasets.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com