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.
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).
Among these methods, the simpler WS method had best median ROC score.
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.
It is observed that both PCAI4 and PCAI8 methods have the best efficiency.
For instance, in applying UDC to the feature sets, RFA methods have the best prediction performance evaluated using testing accuracy and MCC values; however, with respect to performance evaluated using AUC errors RFA is not the best.
The GLM and STL methods have the best sensitivity, with the STL method clearly ahead in all cases.
To make comparison between our method and these two cost-sensitive methods, we select the cost settings with which both cost-sensitive methods have the best F-measure values.
Over the range of simulation scenarios considered, the Harbord, Peters and both Egger dispersion (D-se & D-var) methods would appear to have best overall performance.
