stability of welding process is the prerequisite and assurance for ideal joint. The structure of stability model and its optimization are the key to on-line evaluation technology of arc welding. Characteristic paramet...stability of welding process is the prerequisite and assurance for ideal joint. The structure of stability model and its optimization are the key to on-line evaluation technology of arc welding. Characteristic parameters are extracted from the single transfer period while variation coefficients of the characteristic parameters are concerned for whole welding process of continuous drop transfer. Based on the characteristic parameters and their variation coefficients, stability model of short-circuit arc welding process is established by partial-least-square regression （PLSR） that can overcome multicollinearity of input parameters. The experiment results show that this model can meet the requirement of accuracy.展开更多
Optical remote sensing(RS)with robust algorithms is needed for accurate assessment of crop canopy features.Despite intensive studies on algorithms,their performance using RS needs to be improved.We evaluated five diff...Optical remote sensing(RS)with robust algorithms is needed for accurate assessment of crop canopy features.Despite intensive studies on algorithms,their performance using RS needs to be improved.We evaluated five different algorithms(partial-least-squares regression(PLSR),support vector regression(SVR),random forest regression(RFR),locally-weighted-PLSR(PLSRLW)and PLSR with feature selection(PLSRFS))for rapid assessment of leaf area index(LAI)and canopy water content(CWC)for rice canopies using canopy reflectance spectra over visible to short-wave infrared region.Two pooled datasets of LAI(600)and CWC(480)were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season.The performance of each algorithm was evaluated using coefficient of determination(R^(2)).Results showed that PLSRLW performed more accurately than other algorithms with R^(2) values 0.77 and 0.66 for LAI and CWC,respectively.We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model.The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples,which has similar canopy structure as the test sample.Subsetting of the canopy spectral data results large error values in test dataset,therefore the use of entire season canopy spectral data should be used for model calibration.展开更多
文摘stability of welding process is the prerequisite and assurance for ideal joint. The structure of stability model and its optimization are the key to on-line evaluation technology of arc welding. Characteristic parameters are extracted from the single transfer period while variation coefficients of the characteristic parameters are concerned for whole welding process of continuous drop transfer. Based on the characteristic parameters and their variation coefficients, stability model of short-circuit arc welding process is established by partial-least-square regression （PLSR） that can overcome multicollinearity of input parameters. The experiment results show that this model can meet the requirement of accuracy.
文摘Optical remote sensing(RS)with robust algorithms is needed for accurate assessment of crop canopy features.Despite intensive studies on algorithms,their performance using RS needs to be improved.We evaluated five different algorithms(partial-least-squares regression(PLSR),support vector regression(SVR),random forest regression(RFR),locally-weighted-PLSR(PLSRLW)and PLSR with feature selection(PLSRFS))for rapid assessment of leaf area index(LAI)and canopy water content(CWC)for rice canopies using canopy reflectance spectra over visible to short-wave infrared region.Two pooled datasets of LAI(600)and CWC(480)were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season.The performance of each algorithm was evaluated using coefficient of determination(R^(2)).Results showed that PLSRLW performed more accurately than other algorithms with R^(2) values 0.77 and 0.66 for LAI and CWC,respectively.We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model.The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples,which has similar canopy structure as the test sample.Subsetting of the canopy spectral data results large error values in test dataset,therefore the use of entire season canopy spectral data should be used for model calibration.
