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The method performs very well, and often outperforms existing methods.
The developed method performs very well with catchments larger than 45 km2 with coefficient of correlation of 0.92.
Monte Carlo simulations of a time-varying, second-order system mimicking variations of reex stiffness dynamics with ankle position demonstrated that the method performs very well.
The KMVCE method performs very well on a set of real-life and simulated datasets, when compared with standard kernel-based novelty detection methods.
Experimental results and a comparison with the latest methods show that the proposed method performs very well both in the running speed and in the quality of the solutions obtained.
Also, the developed method performs very well with either event-based or return-period-based peak discharge prediction and it can be considered a good alternative to the rational method.
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On average, real sample concentration uncertainties were lower than 10%, indicating that the analytical method performed very well.
The photogrammetry method performed very well to track the lateral and vertical displacements at the points on the grid surface as well as the deformed shape of the hinging regions, but the results of secondary calculations, such as rotations of the column, had limited success.
The method performed very well in various target detection methods and achieved good detection results on different data sets such as PASCSAL VOC benchmarks and INRIA Person.
The ORB method performed very well for the real-world pitch variation conditions and was seen to be robust for pitch changes up to ranges from −20 to +20%.
Although in the present form, the method performed very well on signals where small information about the nature of the signals are apriori known, e.g., if it is electrocardiogram (ECG) signal, speech signal or mechanical vibrations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com