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We conclude with a discussion of the method and a comparison to the methods described in previous studies.
The obtained results showed improvement of the objective functions using the CRO method in comparison to the methods used previously for optimization of the considered refrigeration system.
In order to allow a comparison to the methods based on Polynomial Chaos expansions, cf. Sections 4 and 5, we omit the treatment of measurements and only consider the state estimation.
In comparison to the methods described above, simulation environments provide a realistic environment in which users can explore simplified versions of both realistic and highly hypothetical situations [ 65].
Although other methods exist to predict 14-3-3 14-3-3 14-3-3 (Chan et al., 2011; Panni et al., 2011), no software or pre-computed predictions were availabinding comparisiteso the methods developed here.
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A usability evaluation was carried out to measure the usefulness and effectiveness of the tool in comparison to the method, and the results showed that the tool has a positive impact on multimedia design.
Additionally, the estimated mean CBF value was identical to the assumed CBF value, independent of shift time; and particularly, the magnitude of noise on CBF was not enhanced in comparison to the method without ATB correction, i.e., BFMF.
For the comparison to the method of Hou et al., binder and non-binder data in Hou's publication were used.
In comparison to the method published by Ogawa et al. [ 4], the approach described above is faster and considerably easier to realise.
We have not performed any direct comparison to the method described by Roberts et al. (2011) and implemented in Cufflinks (Trapnell et al., 2010).
In comparison to the probabilistic methods, these methods require more processing overhead but their advantage is providing higher accuracy.
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