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The experimental result demonstrates that the proposed scheme obtains the best average error of 0.0930° in latitude/longitude in comparison with the best track data.
Experimental results on infrared satellite cloud images show that the model can achieve the best average error of 0.1223° (about 13.58 km) on both latitude and longitude in comparison with the best track data.
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Comparison of these correlations against the total of 714 data indicated that the correlation proposed by Chen-Fang is the best with an average error of −0.44% and a standard deviation of 6.4%.
The blue curve is generated with one correct cloud and accordingly shows the best performance with an average error of 1.33 m.
Results show an average error of about 8.42% for best-case delay while allowing for very fast analysis in comparison to HSPICE.
On the other hand, in case of high noise, spatial averages with 3×2 cells (green line) reaches performance as good as the best one: a mean localization error of 2.5 m means that the average error of 200 experiments is of just one cell.
To minimize the average error of the conditional spectral probabilities reported by TD-GF, the best value of log(K) is the average of the log ratio.
The model was trained on a dataset of 123 oligomers to achieve a best fit equation (1); (Figure 1), which gave a correlation coefficient (r) of 0.98 and an average error of 1.36°C (data provided in Table S1).
The parameter combination that best fits the whole set of experiments, including both column collapses and convective plumes, results into an average error of about 10%.
On my spreadsheet that's an average error of 20%.
Average error of bad pixels per image.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com