In blast furnace （BF） iron-making process, the hot metal silicon content was usually used to measure the quality of hot metal and to reflect the thermal state of BF. Principal component analysis （PCA） and partial ...In blast furnace （BF） iron-making process, the hot metal silicon content was usually used to measure the quality of hot metal and to reflect the thermal state of BF. Principal component analysis （PCA） and partial least- square （PLS） regression methods were used to predict the hot metal silicon content. Under the conditions of BF rela- tively stable situation, PCA and PLS regression models of hot metal silicon content utilizing data from Baotou Steel No. 6 BF were established, which provided the accuracy of 88.4% and 89.2%. PLS model used less variables and time than principal component analysis model, and it was simple to calculate. It is shown that the model gives good results and is helpful for practical production.展开更多
Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used parti...Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.展开更多
As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring sys...As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.展开更多
Purpose:This paper aims to examine how the adoption decision of the internet banking in North Cyprus would be affected based on the following dimensions;the technology features,the personal characteristics,the social ...Purpose:This paper aims to examine how the adoption decision of the internet banking in North Cyprus would be affected based on the following dimensions;the technology features,the personal characteristics,the social environment and the expected risk.Design/methodology/approach:A self-administered survey was conducted with 291 participants responded to it.The partial least square approach of the structural equation modeling(PLS-SEM)is employed to investigate the direct effects of the proposed factors on the adoption decision.Additionally,the mediation test is used to examine indirect effects.Findings:Results showed that even though the participants appreciated the benefits of the online banking as the perceived usefulness factor exerts the greatest direct effect,they would rather use clear and easy-to-use websites,adding to that their assessments of the usefulness of these services are significantly influenced by the surrounding people’s views and prior experience.This is demonstrated by the total effects of the perceived ease of use and the subjective norm factors,which are greater than the direct effect of the perceived usefulness factor since both of these factors have significant direct and indirect effects mediated by the perceived usefulness factor.The negative impact of the perceived risk factor is weak compared to the previous factors.While the personal innovativeness factor showed the weakest effect among the proposed factors.展开更多
This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic...This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes： arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression （PLSR） is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.展开更多
pH and volatile fatty acids both might affect the further hydrolysis of particulate solid waste, which is the limiting-step of anaerobic digestion. To clarify the individual effects of pH and volatile fatty acids, bat...pH and volatile fatty acids both might affect the further hydrolysis of particulate solid waste, which is the limiting-step of anaerobic digestion. To clarify the individual effects of pH and volatile fatty acids, batch experiments were conducted at fixed pH value （pH 5-9） with or without acetate （20 g/L）. The hydrolysis efficiencies of carbohydrate and protein were evaluated by carbon and nitrogen content of solids, amylase activity and proteinase activity. The trend of carbohydrate hydrolysis with pH was not affected by the addition of acetate, following the sequence ofpH 7〉pH 8〉pH 9〉pH 6〉pH 5; but the inhibition of acetate （20 g/L） was obvious by 10%-60 %. The evolution of residual nitrogen showed that the effect of pH on protein hydrolysis was minor, while the acetate was seriously inhibitory especially at alkali condition by 45%-100 %. The relationship between the factors （pH and acetate） and the response variables was evaluated by partial least square modeling （PLS）. The PLS analysis demonstrated that the hydrolysis of carbohydrate was both affected by pH and acetate, with pH the more important factor. Therefore, the inhibition by acetate on carbohydrate hydrolysis was mainly due to the corresponding decline of pH, but the presence of acetate species, while the acetate species was the absolutely important factor for the hydrolysis of protein.展开更多
Objective To investigate v arious data message of the stator bars condition parameters under the condition that only a few samples are available, especially about correlation information between the nondestructiv...Objective To investigate v arious data message of the stator bars condition parameters under the condition that only a few samples are available, especially about correlation information between the nondestructive parameters and residual breakdown voltage of the stat or bars. Methods Artificial stator bars is designed to simulat e the generator bars. The partial didcharge( PD) and dielectric loss experiments are performed in order to obtain the nondestructive parameters, and the residua l breakdown voltage acquired by AC damage experiment. In order to eliminate the dimension effect on measurement data, raw data is preprocessed by centered-compr ess. Based on the idea of extracting principal components, a partial least squar e (PLS) method is applied to screen and synthesize correlation information betwe en the nondestructive parameters and residual breakdown voltage easily. Moreover , various data message about condition parameters are also discussed. Re sults Graphical analysis function of PLS is easily to understand vario us data message of the stator bars condition parameters. The analysis Results ar e consistent with result of aging testing. Conclusion The meth od can select and extract PLS components of condition parameters from sample dat a, and the problems of less samples and multicollinearity are solved effectively in regression analysis.展开更多
Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution...Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.展开更多
Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, co...Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.展开更多
Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics...Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.展开更多
In order to evaluate the general situation and find special problems of the freeway incident management system, an evaluation model is proposed. First, the expert appraisal approach is used to select the primary evalu...In order to evaluate the general situation and find special problems of the freeway incident management system, an evaluation model is proposed. First, the expert appraisal approach is used to select the primary evaluation index. As a result, 81 indices and the hierarchical structures of the index such as the object layer, the sub-object layer, the criterion layer and the index layer are determined. Then, based on the fuzzy characteristics of each index layer, the analytical hierarchy process（AHP）and the fuzzy comprehensive evaluation are applied to generate the weight and the satisfaction of the index and the criterion layers. When analyzing the relationship between the sub-object layer and the object layer, it is easy to find that the number of sub-objects is too large and sub-objects are significantly redundant. The partial least square （PLS） is proposed to solve the problems. Finally, an application example, whose result has already been accepted and employed as the indication of a new project in improving incident management, is introduced and the result verifies the feasibility and efficiency of the model.展开更多
This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyze...This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS).Pearson's linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compound scould be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized,representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (-0.785), and β-ionone (-0.763). On the basis of these 10 compounds, a model (correlation coefficient of89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjingtea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MStechnique.展开更多
Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large are...Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350-2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R2) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R2in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.展开更多
Soil quality monitoring is important in precision agriculture.This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods.A total of 111 soil sampl...Soil quality monitoring is important in precision agriculture.This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods.A total of 111 soil samples were collected from 11 typical sites of apple orchards,and the croplands surrounding them.Near-infrared(NIR) and mid-infrared(MIR) spectra,combined with partial least square regression,were used to predict the soil parameters,including organic matter(OM) content,pH,and the contents of As,Cu,Zn,Pb,and Cr.Organic matter and pH were closely correlated with As and the heavy metals.The NIR model showed a high prediction accuracy for the determination of OM,pH,and As,with correlation coefficients(r) of 0.89,0.89,and 0.90,respectively.The predictions of these three parameters by MIR showed reduced accuracy,with r values of 0.77,0.84,and 0.92,respectively.The heavy metals could also be measured by spectroscopy due to their correlation with organic matter.Both NIR and MIR had high correlation coefficients for the determination of Cu,Zn,and Cr,with standard errors of prediction of 2.95,10.48,and 9.49 mg kg-1 for NIR and 3.69,5.84,and 6.94 mg kg-1 for MIR,respectively.Pb content behaved differently from the other parameters.Both NIR and MIR underestimated Pb content,with r values of 0.67 and 0.56 and standard errors of prediction of 3.46 and 2.99,respectively.Cu and Zn had a higher correlation with OM and pH and were better predicted than Pb and Cr.Thus,NIR spectra could accurately predict several soil parameters,metallic and nonmetallic,simultaneously,and were more feasible than MIR in analyzing soil parameters in the study area.展开更多
Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic ...Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.展开更多
The use of near infrared （NIR） spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800-2500 nm by InGaAs detector, was used...The use of near infrared （NIR） spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800-2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts （including three latitudes and three longitudes） were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes （i, ii, iii） and latitudes （A, B, C）. For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression （PLSR） models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction （MSC） pretreatment, with correlation coefficient of cross-validation γcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.展开更多
In cold rolling process,the flatness actuator efficiency is the basis of the flatness control system.The precision of flatness is determined by the setpoints of flatness actuators.In the presence of modeling uncertain...In cold rolling process,the flatness actuator efficiency is the basis of the flatness control system.The precision of flatness is determined by the setpoints of flatness actuators.In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process,it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately.Based on the production data,a method to obtain the flatness actuator efficiency by using partial least square(PLS)combined with orthogonal signal correction(OSC)was adopted.Compared with the experiential method and principal component analysis method,the OSC-PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand.Furthermore,kernel partial least square combined with artificial neural network(KPLS-ANN)was proposed to predict the flatness values and optimize the setpoints of flatness actuators.Compared with KPLS or ANN,KPLS-ANN shows the best predictive ability.The root mean square error,mean absolute error and mean absolute percentage error are 0.51 IU,0.34 IU and 0.09,respectively.After the setpoints of flatness actuators are optimized,KPLS-ANN shows better optimization ability.The result in an average flatness standard deviation is 2.22 IU,while the unoptimized value is 4.10 IU.展开更多
Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect ...Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.展开更多
The near infrared （NIR） spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectrosco...The near infrared （NIR） spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content （SSC） and acidity （pH） of intact loquats. Two cultivars of loquats （Dahongpao and Jiajiaozhong） harvested from two orchards （Tangxi and Chun＇an, Zhejiang, China） were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats （100 samples of each cultivar from each orchard） were used in this study. Relationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square （PLS） method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction （MSC）, and the standard normal variate （SNV）. Three separate spectral windows identified as full NIR （800-2500 nm）, short NIR （800-1100 rim）, and long NIR （1100-2500 nm） were studied in factorial combination with the preprocessing options. The models gave relatively good predictions of the SSC of loquats, with root mean square error of prediction （RMSEP） values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun＇an-Dahongpao, and Chun＇an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.展开更多
A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measure- ment model.The...A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measure- ment model.The flow pattern identification was achieved by combining the fuzzy pattern recognition technique and the crude cross-sectional image reconstructed by the simple back projection algorithm.The genetic algorithm and the partial least square method were applied to develop the voidage measurement models.Experimental results show that the proposed method is effective.It can overcome the influence of flow pattern on the voidage measure- ment,and also has the advantages of simplicity and speediness.展开更多
基金Item Sponsored by National Natural Science Foundation of China(51064019)Natural Science Foundation of Inner Mongolia of China(20010MS0911,NJzy08075)
文摘In blast furnace （BF） iron-making process, the hot metal silicon content was usually used to measure the quality of hot metal and to reflect the thermal state of BF. Principal component analysis （PCA） and partial least- square （PLS） regression methods were used to predict the hot metal silicon content. Under the conditions of BF rela- tively stable situation, PCA and PLS regression models of hot metal silicon content utilizing data from Baotou Steel No. 6 BF were established, which provided the accuracy of 88.4% and 89.2%. PLS model used less variables and time than principal component analysis model, and it was simple to calculate. It is shown that the model gives good results and is helpful for practical production.
基金supported by the 948 Program of the State Forestry Administration (2009-4-43)the National Natura Science Foundation of China (No.30870420)
文摘Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.
基金supported by National Key Scientific Instrument and Equipment Development Project of China,Grant Nos.2013YQ220643the National 863 Program of China,Grant Nos.2014AA06A503.
文摘As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.
文摘Purpose:This paper aims to examine how the adoption decision of the internet banking in North Cyprus would be affected based on the following dimensions;the technology features,the personal characteristics,the social environment and the expected risk.Design/methodology/approach:A self-administered survey was conducted with 291 participants responded to it.The partial least square approach of the structural equation modeling(PLS-SEM)is employed to investigate the direct effects of the proposed factors on the adoption decision.Additionally,the mediation test is used to examine indirect effects.Findings:Results showed that even though the participants appreciated the benefits of the online banking as the perceived usefulness factor exerts the greatest direct effect,they would rather use clear and easy-to-use websites,adding to that their assessments of the usefulness of these services are significantly influenced by the surrounding people’s views and prior experience.This is demonstrated by the total effects of the perceived ease of use and the subjective norm factors,which are greater than the direct effect of the perceived usefulness factor since both of these factors have significant direct and indirect effects mediated by the perceived usefulness factor.The negative impact of the perceived risk factor is weak compared to the previous factors.While the personal innovativeness factor showed the weakest effect among the proposed factors.
文摘This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes： arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression （PLSR） is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.
文摘pH and volatile fatty acids both might affect the further hydrolysis of particulate solid waste, which is the limiting-step of anaerobic digestion. To clarify the individual effects of pH and volatile fatty acids, batch experiments were conducted at fixed pH value （pH 5-9） with or without acetate （20 g/L）. The hydrolysis efficiencies of carbohydrate and protein were evaluated by carbon and nitrogen content of solids, amylase activity and proteinase activity. The trend of carbohydrate hydrolysis with pH was not affected by the addition of acetate, following the sequence ofpH 7〉pH 8〉pH 9〉pH 6〉pH 5; but the inhibition of acetate （20 g/L） was obvious by 10%-60 %. The evolution of residual nitrogen showed that the effect of pH on protein hydrolysis was minor, while the acetate was seriously inhibitory especially at alkali condition by 45%-100 %. The relationship between the factors （pH and acetate） and the response variables was evaluated by partial least square modeling （PLS）. The PLS analysis demonstrated that the hydrolysis of carbohydrate was both affected by pH and acetate, with pH the more important factor. Therefore, the inhibition by acetate on carbohydrate hydrolysis was mainly due to the corresponding decline of pH, but the presence of acetate species, while the acetate species was the absolutely important factor for the hydrolysis of protein.
文摘Objective To investigate v arious data message of the stator bars condition parameters under the condition that only a few samples are available, especially about correlation information between the nondestructive parameters and residual breakdown voltage of the stat or bars. Methods Artificial stator bars is designed to simulat e the generator bars. The partial didcharge( PD) and dielectric loss experiments are performed in order to obtain the nondestructive parameters, and the residua l breakdown voltage acquired by AC damage experiment. In order to eliminate the dimension effect on measurement data, raw data is preprocessed by centered-compr ess. Based on the idea of extracting principal components, a partial least squar e (PLS) method is applied to screen and synthesize correlation information betwe en the nondestructive parameters and residual breakdown voltage easily. Moreover , various data message about condition parameters are also discussed. Re sults Graphical analysis function of PLS is easily to understand vario us data message of the stator bars condition parameters. The analysis Results ar e consistent with result of aging testing. Conclusion The meth od can select and extract PLS components of condition parameters from sample dat a, and the problems of less samples and multicollinearity are solved effectively in regression analysis.
基金This work was supported by the National High-tech Research and Development Program of China (No. 2003AA412110).
文摘Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.
基金Supported by "863" Program of P. R. China(2002AA2Z4291)
文摘Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.
文摘Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.
文摘In order to evaluate the general situation and find special problems of the freeway incident management system, an evaluation model is proposed. First, the expert appraisal approach is used to select the primary evaluation index. As a result, 81 indices and the hierarchical structures of the index such as the object layer, the sub-object layer, the criterion layer and the index layer are determined. Then, based on the fuzzy characteristics of each index layer, the analytical hierarchy process（AHP）and the fuzzy comprehensive evaluation are applied to generate the weight and the satisfaction of the index and the criterion layers. When analyzing the relationship between the sub-object layer and the object layer, it is easy to find that the number of sub-objects is too large and sub-objects are significantly redundant. The partial least square （PLS） is proposed to solve the problems. Finally, an application example, whose result has already been accepted and employed as the indication of a new project in improving incident management, is introduced and the result verifies the feasibility and efficiency of the model.
文摘This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS).Pearson's linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compound scould be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized,representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (-0.785), and β-ionone (-0.763). On the basis of these 10 compounds, a model (correlation coefficient of89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjingtea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MStechnique.
基金Supported by the National Natural Science Foundation of China (No. 41071159)the Cooperative Ecosystem Studies UnitNational Resources Conservation Service (NRCS), USA
文摘Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350-2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R2) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R2in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.
基金Supported by the Major Science and Technology Program for Water Pollution Control and Treatment in China(No.2008ZX07425-001)
文摘Soil quality monitoring is important in precision agriculture.This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods.A total of 111 soil samples were collected from 11 typical sites of apple orchards,and the croplands surrounding them.Near-infrared(NIR) and mid-infrared(MIR) spectra,combined with partial least square regression,were used to predict the soil parameters,including organic matter(OM) content,pH,and the contents of As,Cu,Zn,Pb,and Cr.Organic matter and pH were closely correlated with As and the heavy metals.The NIR model showed a high prediction accuracy for the determination of OM,pH,and As,with correlation coefficients(r) of 0.89,0.89,and 0.90,respectively.The predictions of these three parameters by MIR showed reduced accuracy,with r values of 0.77,0.84,and 0.92,respectively.The heavy metals could also be measured by spectroscopy due to their correlation with organic matter.Both NIR and MIR had high correlation coefficients for the determination of Cu,Zn,and Cr,with standard errors of prediction of 2.95,10.48,and 9.49 mg kg-1 for NIR and 3.69,5.84,and 6.94 mg kg-1 for MIR,respectively.Pb content behaved differently from the other parameters.Both NIR and MIR underestimated Pb content,with r values of 0.67 and 0.56 and standard errors of prediction of 3.46 and 2.99,respectively.Cu and Zn had a higher correlation with OM and pH and were better predicted than Pb and Cr.Thus,NIR spectra could accurately predict several soil parameters,metallic and nonmetallic,simultaneously,and were more feasible than MIR in analyzing soil parameters in the study area.
基金Project supported by the National Natural Science Foundation of China (No. 30270773), and the Teaching and Research Award Pro-gram for Outstanding Young Teachers in Higher Education Institu-tions & the Specialized Research Fund for the Doctoral Program o
文摘Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.
基金the National Natural Science Foundation of China (No. 30671197)the Program for New Century Excellent Talents in University, China (No. NCET-04-0524)
文摘The use of near infrared （NIR） spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800-2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts （including three latitudes and three longitudes） were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes （i, ii, iii） and latitudes （A, B, C）. For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression （PLSR） models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction （MSC） pretreatment, with correlation coefficient of cross-validation γcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.
基金This study is financially supported by the National Key Research and Development Program of China(No.2017YFB0304100)the National Natural Science Foundation of China(Nos.51774084,51704067,and 51634002)+1 种基金the Fundamental Research Funds for the Central Universities(Nos.N160704004,N170708020,and N2004010)Liaoning Revitalization Talents Program(XLYC1907065).
文摘In cold rolling process,the flatness actuator efficiency is the basis of the flatness control system.The precision of flatness is determined by the setpoints of flatness actuators.In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process,it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately.Based on the production data,a method to obtain the flatness actuator efficiency by using partial least square(PLS)combined with orthogonal signal correction(OSC)was adopted.Compared with the experiential method and principal component analysis method,the OSC-PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand.Furthermore,kernel partial least square combined with artificial neural network(KPLS-ANN)was proposed to predict the flatness values and optimize the setpoints of flatness actuators.Compared with KPLS or ANN,KPLS-ANN shows the best predictive ability.The root mean square error,mean absolute error and mean absolute percentage error are 0.51 IU,0.34 IU and 0.09,respectively.After the setpoints of flatness actuators are optimized,KPLS-ANN shows better optimization ability.The result in an average flatness standard deviation is 2.22 IU,while the unoptimized value is 4.10 IU.
基金the National Natural Science Foundation of China (41101395, 41071276, 31071324)the Beijing Municipal Natural Science Foundation, China (4122032)the National Basic Research Program of China (2011CB311806)
文摘Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.
基金Project supported by the National Natural Science Foundation of China(No.30825027)the National Key Technology R&D Program of China(No.2006BAD11A12)
文摘The near infrared （NIR） spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content （SSC） and acidity （pH） of intact loquats. Two cultivars of loquats （Dahongpao and Jiajiaozhong） harvested from two orchards （Tangxi and Chun＇an, Zhejiang, China） were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats （100 samples of each cultivar from each orchard） were used in this study. Relationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square （PLS） method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction （MSC）, and the standard normal variate （SNV）. Three separate spectral windows identified as full NIR （800-2500 nm）, short NIR （800-1100 rim）, and long NIR （1100-2500 nm） were studied in factorial combination with the preprocessing options. The models gave relatively good predictions of the SSC of loquats, with root mean square error of prediction （RMSEP） values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun＇an-Dahongpao, and Chun＇an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.
基金Supported by the National lqatural Science Foundation of China （Nos.50576084 and 60532020）.
文摘A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measure- ment model.The flow pattern identification was achieved by combining the fuzzy pattern recognition technique and the crude cross-sectional image reconstructed by the simple back projection algorithm.The genetic algorithm and the partial least square method were applied to develop the voidage measurement models.Experimental results show that the proposed method is effective.It can overcome the influence of flow pattern on the voidage measure- ment,and also has the advantages of simplicity and speediness.
