The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensembl...The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction ...The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.展开更多
In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation meth...In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.展开更多
Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communit...Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting.展开更多
This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-sco...This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.展开更多
Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids ...Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time (t) and solvent load (M). Then the predicted procedure was carried out using the following data: 1 ) based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2) Locally weighted regression (LWR) code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process.展开更多
Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coeff...Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.展开更多
在无线传感器网络中,针对接收信号强度指示(Received Signal Strength Indication,RSSI)在煤矿井下长距离巷道内信号衰减快、测距精度偏差大等问题,提出了一种基于RSSI的高斯滤波加权质心定位算法。采用高斯滤波对采集的RSSI值进行修正...在无线传感器网络中,针对接收信号强度指示(Received Signal Strength Indication,RSSI)在煤矿井下长距离巷道内信号衰减快、测距精度偏差大等问题,提出了一种基于RSSI的高斯滤波加权质心定位算法。采用高斯滤波对采集的RSSI值进行修正,一定程度上减轻环境造成的影响。将RSSI测距算法与改进加权质心算法相结合,得出待测节点坐标位置。仿真试验表明,该改进算法与原有定位算法相比,定位误差明显降低,可基本满足煤矿井下人员的安全生产和定位需求。展开更多
Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related lo...Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables.Specifically,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement algorithm.The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail.The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column.It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.展开更多
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number 105.08-2019.03.
文摘The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
文摘The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.
文摘In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.
基金supported by the European Commission within FP7-THEME 6(Grant No.244104)the Natural Environment Research Council(NERC)of the UK(Grant No.NE/J005541/1)the Ministry of Science and Technology(MOST)of Taiwan(Grant No.MOST 104-2221-E-006-183)
文摘Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting.
基金supported by the Fundamental Research Funds for the Central Universities (QN0914)
文摘This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.
基金National Nature Science Foundation of China(surface project)(81173563)
文摘Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time (t) and solvent load (M). Then the predicted procedure was carried out using the following data: 1 ) based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2) Locally weighted regression (LWR) code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process.
基金the National Natural Science Foundation of China (No.60075001) and Xi'anJiaotong University Natural Science Foundation.
文摘Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
文摘在无线传感器网络中,针对接收信号强度指示(Received Signal Strength Indication,RSSI)在煤矿井下长距离巷道内信号衰减快、测距精度偏差大等问题,提出了一种基于RSSI的高斯滤波加权质心定位算法。采用高斯滤波对采集的RSSI值进行修正,一定程度上减轻环境造成的影响。将RSSI测距算法与改进加权质心算法相结合,得出待测节点坐标位置。仿真试验表明,该改进算法与原有定位算法相比,定位误差明显降低,可基本满足煤矿井下人员的安全生产和定位需求。
基金the National Key Research and Development Program of China(No.2016YFC0301404)the National Natural Science Foundation of China(Nos.51379198 and 61903352)+5 种基金the Natural Science Foundation of Zhejiang Province,China(No.LQ19F030007)the Natural Science Foundation of Jiangsu Province,China(No.BK20180594)the Project of Department of Education of Zhejiang Province,China(No.Y202044960)the China Postdoctoral Science Foundation(No.2020M671721)the Foundation of Key Laboratory of Advanced Process Control for Light Industry(No.APCLI1803)the Fundamental Research Funds for the Provincial Universities of Zhejiang,China(Nos.2021YW18 and 2021YW80)。
文摘Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables.Specifically,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement algorithm.The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail.The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column.It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.