Ping′ou hybrid hazelnut is produced by cross cultivation and is widely cultivated in northern China with good development prospects.Based on a field experiment of fertilizer efficiency,the leaf spectral reflectance a...Ping′ou hybrid hazelnut is produced by cross cultivation and is widely cultivated in northern China with good development prospects.Based on a field experiment of fertilizer efficiency,the leaf spectral reflectance and leaf potassium(K)concentration were measured with different quantities of K fertilizer applied at four fruit growth stages(fruit setting stage,fruit rapid growth stage,fruit fat-change stage,and fruit near-maturity stage)of Ping′ou hybrid hazelnut in 2019.Spectral parameters that were significantly correlated with leaf K concentration were selected using Pearson correlation analysis,and spectral parameter estimation models of leaf K concentration were established by employing six different modelling methods(exponential function,power function,logarithmic function,linear function,quadratic function,and cubic function).The results indicated that at the fruit setting period,leaf K concentration was significantly correlated with Dy(spectra slope of yellow edge),Rg(reflectance of the green peak position),λo(red valley position),SDb(blue edge area),SDr/SDb(where SDr represents red edge area),and(SDr–SDb)/(SDr+SDb)(P<0.01).There were significant correlations of leaf K concentration with Dy,Rg,SDb,Rg/Ro(where Ro is the reflectance of the red valley position),and(Rg–Ro)/(Rg+Ro)at the fruit rapid growth stage(P<0.01).Further,significant correlations of leaf K concentration with Rg,Ro,RNIR/Green,and RNIR/Blue were obtained at the fruit fat-change period(P<0.01).Finally,leaf K concentration showed significant correlations with Dr,Rg,Ro,SDy(yellow edge area),and SDr at the fruit near-maturity stage(P<0.01).Through a cubic function analysis,regression estimation model of leaf K concentration with highest fitting degree(R2)values at the four fruit growth stages was established.The findings in this study demonstrated that it is feasible to estimate leaf K concentration of Ping′ou hybrid hazelnut at the various phenological stages of fruit development by establishing regression models between leaf K concentration and spectral parameters.展开更多
Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support ...Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.展开更多
This paper presents three strategies for modeling the regional empirical Tm （the weighted mean tem- perature of the atmosphere） to obtain more accurate determinations in a regional empirical model that is better ada...This paper presents three strategies for modeling the regional empirical Tm （the weighted mean tem- perature of the atmosphere） to obtain more accurate determinations in a regional empirical model that is better adapted to the geographical and climatic characteristics of the applied area. The proposed models utilize data from four radiosonde stations in Guangxi, at Nanning, Guilin, Wuzhou and Baise, over an 11 month period （from Jan. to Nov. of 2011 ）. The experimental results demonstrated the following： （1） there is no significant展开更多
Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over...Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over a large spatial area is not an easy task due to the significant amount of time and manpower required to collect such large-scale datasets.In this study,a hybrid geostatistical approach,named Network Regression Kriging,has been developed to estimate urban traffic volumes by incorporating auxiliary variables such as road type,speed limit,and network accessibility.Since standard kriging is based on Euclidean distances,this study implements road network distances to improve traffic volumes estimations.A case study using 10-year of traffic volume data collected within the city of Edmonton was conducted to demonstrate the robustness of the model developed herein.Results suggest that the proposed hybrid model significantly outperforms the standard kriging method in terms of accuracy by 4.0%overall,especially for a large-scale network.It was also found that the necessary stationarity assumption for kriging did not hold true for a large network whereby separate estimations for each road type performed significantly better than a general estimation for the overall network by 4.12%.展开更多
Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due t...Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output.As the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather conditions.This study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast horizons.Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model.The proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework.We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least squares.Based on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.展开更多
基于响应面法设计方法,进行Nd:YAG激光-熔化极活性气体保护焊(Metal active gas welding,MAG)复合焊高氮钢的平板堆焊试验,获得焊缝熔深、熔宽、余高数据,采用逐步回归法筛选出对焊缝形貌影响显著的因子,建立多元非线性数学回归模型,通...基于响应面法设计方法,进行Nd:YAG激光-熔化极活性气体保护焊(Metal active gas welding,MAG)复合焊高氮钢的平板堆焊试验,获得焊缝熔深、熔宽、余高数据,采用逐步回归法筛选出对焊缝形貌影响显著的因子,建立多元非线性数学回归模型,通过方差分析和回归分析得出该回归模型的R2分别如下:熔深H为0.932,熔宽W为0.915,余高A为0.910,P>F值均小于0.001。模型分析结果表明激光功率、焊接电流、电弧电压和热源间距四个因素的主效应和交互作用对焊缝形貌有着很大的影响,其中对熔深影响最大的主效应是激光功率,交互效应是激光功率与电弧电压;对熔宽影响最大的主效应是焊接电流和电弧电压,交互效应是焊接电流与热源间距、电弧电压与热源间距和激光功率与电弧电压;对余高影响最大的主效应是焊接电流,交互效应是电弧电压与热源间距。试验验证结果表明模拟结果和试验结果相吻合。展开更多
基金the National Natural Science Foundation of China(31960324)。
文摘Ping′ou hybrid hazelnut is produced by cross cultivation and is widely cultivated in northern China with good development prospects.Based on a field experiment of fertilizer efficiency,the leaf spectral reflectance and leaf potassium(K)concentration were measured with different quantities of K fertilizer applied at four fruit growth stages(fruit setting stage,fruit rapid growth stage,fruit fat-change stage,and fruit near-maturity stage)of Ping′ou hybrid hazelnut in 2019.Spectral parameters that were significantly correlated with leaf K concentration were selected using Pearson correlation analysis,and spectral parameter estimation models of leaf K concentration were established by employing six different modelling methods(exponential function,power function,logarithmic function,linear function,quadratic function,and cubic function).The results indicated that at the fruit setting period,leaf K concentration was significantly correlated with Dy(spectra slope of yellow edge),Rg(reflectance of the green peak position),λo(red valley position),SDb(blue edge area),SDr/SDb(where SDr represents red edge area),and(SDr–SDb)/(SDr+SDb)(P<0.01).There were significant correlations of leaf K concentration with Dy,Rg,SDb,Rg/Ro(where Ro is the reflectance of the red valley position),and(Rg–Ro)/(Rg+Ro)at the fruit rapid growth stage(P<0.01).Further,significant correlations of leaf K concentration with Rg,Ro,RNIR/Green,and RNIR/Blue were obtained at the fruit fat-change period(P<0.01).Finally,leaf K concentration showed significant correlations with Dr,Rg,Ro,SDy(yellow edge area),and SDr at the fruit near-maturity stage(P<0.01).Through a cubic function analysis,regression estimation model of leaf K concentration with highest fitting degree(R2)values at the four fruit growth stages was established.The findings in this study demonstrated that it is feasible to estimate leaf K concentration of Ping′ou hybrid hazelnut at the various phenological stages of fruit development by establishing regression models between leaf K concentration and spectral parameters.
基金supported by the National Science Fund for Distinguished Young Scholars under Grant No.71025005the National Natural Science Foundation of China under Grant Nos.91224001 and 71301006+1 种基金National Program for Support of Top-Notch Young Professionalsthe Fundamental Research Funds for the Central Universities in BUCT
文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.
基金supported by the National Natural Foundation of China(4106400141071294)+1 种基金the Natural Science Foundation of Guangxi(2012GXNSFAA053183)Guangxi Key Laboratory of Spatial Information and Geomatics(1103108-06)
文摘This paper presents three strategies for modeling the regional empirical Tm （the weighted mean tem- perature of the atmosphere） to obtain more accurate determinations in a regional empirical model that is better adapted to the geographical and climatic characteristics of the applied area. The proposed models utilize data from four radiosonde stations in Guangxi, at Nanning, Guilin, Wuzhou and Baise, over an 11 month period （from Jan. to Nov. of 2011 ）. The experimental results demonstrated the following： （1） there is no significant
文摘Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over a large spatial area is not an easy task due to the significant amount of time and manpower required to collect such large-scale datasets.In this study,a hybrid geostatistical approach,named Network Regression Kriging,has been developed to estimate urban traffic volumes by incorporating auxiliary variables such as road type,speed limit,and network accessibility.Since standard kriging is based on Euclidean distances,this study implements road network distances to improve traffic volumes estimations.A case study using 10-year of traffic volume data collected within the city of Edmonton was conducted to demonstrate the robustness of the model developed herein.Results suggest that the proposed hybrid model significantly outperforms the standard kriging method in terms of accuracy by 4.0%overall,especially for a large-scale network.It was also found that the necessary stationarity assumption for kriging did not hold true for a large network whereby separate estimations for each road type performed significantly better than a general estimation for the overall network by 4.12%.
基金supported by the Khalifa University of Science and Technology under Award No.RC2 DSO and the Advanced Power and Energy Center.
文摘Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power plants.These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output.As the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather conditions.This study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast horizons.Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model.The proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework.We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least squares.Based on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
文摘基于响应面法设计方法,进行Nd:YAG激光-熔化极活性气体保护焊(Metal active gas welding,MAG)复合焊高氮钢的平板堆焊试验,获得焊缝熔深、熔宽、余高数据,采用逐步回归法筛选出对焊缝形貌影响显著的因子,建立多元非线性数学回归模型,通过方差分析和回归分析得出该回归模型的R2分别如下:熔深H为0.932,熔宽W为0.915,余高A为0.910,P>F值均小于0.001。模型分析结果表明激光功率、焊接电流、电弧电压和热源间距四个因素的主效应和交互作用对焊缝形貌有着很大的影响,其中对熔深影响最大的主效应是激光功率,交互效应是激光功率与电弧电压;对熔宽影响最大的主效应是焊接电流和电弧电压,交互效应是焊接电流与热源间距、电弧电压与热源间距和激光功率与电弧电压;对余高影响最大的主效应是焊接电流,交互效应是电弧电压与热源间距。试验验证结果表明模拟结果和试验结果相吻合。
