Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the anal...Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.展开更多
Through cooperative research between the government,the private sector, mining companies and equipment manufacturers,considerable progress had been made during the last decade in studying the mechanics of strata failu...Through cooperative research between the government,the private sector, mining companies and equipment manufacturers,considerable progress had been made during the last decade in studying the mechanics of strata failure and acquiring the know- ledge needed to develop an integrated monitoring system for assessing local roof stability. Because of higher geotechnical risks in retreat mining operations,it was both important to develop panel layout designs that control convergence and stress and to monitor ground response during operations to verify designs and provide warning of impending stability problems.For detecting both localized roof stability problems and global overburden col- lapse mechanisms,the proposes an integrated panel-wide monitoring system which com- bines the capabilities of load rate monitoring of mobile roof supports (MRSs) with deforma- tion measurements using an extensive array of sensors located near the mining face and throughout the panel.Two monitoring methods for the detection of localized roof stability problems have been evaluated on the basis of mine measurements and numerical model- ing considerations.These are load rate monitoring of the hydraulic cylinders of mobile roof support (MRS) and re mote monitoring of roof movements.Analyses of field data in retreat sections show that roof instabilities are influenced by: (1) pillar failure,(2) pillar yielding,(3) mine seismicity (bumps),(4) geologic structures,and (5) panel layout designs and practice. Pillar yielding and unloading can be conveniently monitored by the load rate monitoring device,but to detect impending localized roof falls,additional ground deformation meas- urements are needed near the mining face.By increasing the number of deformation measurements in the entire panel,additional safeguards can be achieved for detecting overburden collapse mechanisms while continuously monitoring local roof stability close to the retreat line.展开更多
In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） ...In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.展开更多
基金Supported by the National High-Technology Research and Development Program of China under Grant Nos 2014AA06A513 and 2013AA065502the National Natural Science Foundation of China under Grant No 61378041the Anhui Province Outstanding Youth Science Fund of China under Grant No 1508085JGD02
文摘Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.
基金National Institute of Occupational Safety and Health and Fletcher
文摘Through cooperative research between the government,the private sector, mining companies and equipment manufacturers,considerable progress had been made during the last decade in studying the mechanics of strata failure and acquiring the know- ledge needed to develop an integrated monitoring system for assessing local roof stability. Because of higher geotechnical risks in retreat mining operations,it was both important to develop panel layout designs that control convergence and stress and to monitor ground response during operations to verify designs and provide warning of impending stability problems.For detecting both localized roof stability problems and global overburden col- lapse mechanisms,the proposes an integrated panel-wide monitoring system which com- bines the capabilities of load rate monitoring of mobile roof supports (MRSs) with deforma- tion measurements using an extensive array of sensors located near the mining face and throughout the panel.Two monitoring methods for the detection of localized roof stability problems have been evaluated on the basis of mine measurements and numerical model- ing considerations.These are load rate monitoring of the hydraulic cylinders of mobile roof support (MRS) and re mote monitoring of roof movements.Analyses of field data in retreat sections show that roof instabilities are influenced by: (1) pillar failure,(2) pillar yielding,(3) mine seismicity (bumps),(4) geologic structures,and (5) panel layout designs and practice. Pillar yielding and unloading can be conveniently monitored by the load rate monitoring device,but to detect impending localized roof falls,additional ground deformation meas- urements are needed near the mining face.By increasing the number of deformation measurements in the entire panel,additional safeguards can be achieved for detecting overburden collapse mechanisms while continuously monitoring local roof stability close to the retreat line.
文摘In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.
