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The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance – achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys.
Among these methods, the simpler WS method had best median ROC score.
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Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.
The average recovery (ER%) of GA was 98.13% with desirability of 0.997, while the present method has best operational performance like wide linear range 8 6000 ng mL−1 with a Limit of detection (LOD) of 1.377 ng mL−1, limit of quantification (LO Q 4.591 ng mL−1 and precision (<3.50% RSD).
The basic idea exploited in this paper is that the most significant (good) results of all applied methods have best overlap only for certain levels of thresholds.
Overall, the EFr (Rg) method had the best (second best) performance of the four upscaling methods.
Generally, on one method's approach-specific dataset, this method should have best or comparable performances than other methods, so that, the biological characteristics assumed by this given method would significantly displayed on these datasets.
This analysis indicated that the MLS method had the best predictive capability.
Experimental results demonstrated that the OSC-WPT-PLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.
Experimental results demonstrated that the DOSCWPTPLS method had the best result among the four methods and was successful even when there was severe overlap of voltammograms.
Experimental results demonstrated that the WPTERRN method had the best performance among the six methods and the two ANN methods had the clear superiority over the three factor-based method.
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