This work presents the physical and thermal characterization of the dura palm kernel powder of Cameroon for their use as fillers for polymers composites. The powders of palm kernel were obtained using a percussion gri...This work presents the physical and thermal characterization of the dura palm kernel powder of Cameroon for their use as fillers for polymers composites. The powders of palm kernel were obtained using a percussion grinder mill with an industrial microniser which allowed obtaining a powder less than 50 μm with an apparent density between 0,505 ≤ ρ ≤ 0,680 g/cm3 at 1.56 of relative humidity. The infrared of the powder of palm kernel shows the presence of phenols groups with a large band around 3341 cm-1, -C-H at 2917.02 cm-1 and -C-O at 1040 cm-1 as the main peaks. The polyvinyl chloride of infrared obtained shows the presence of -C-Cl, -CH2 and CH as the mains peaks. The infrared of 12.5% of palm kernel powder with polyvinyl chloride shows an increase of the CH2 and CH bonds and a decrease of the -OH bonds. Thermogravimetric analysis and differential scanning calorimetric analysis of powders, polyvinyl chloride and mixture showed that the mixing powders are intermediate between the polyvinyl chloride and palm kernel powder. The powder decreased the phase temperatures of the mixture from 98.58℃ to 95℃ for the glass transition temperature and from 515℃ to 459℃ for the crystallization temperature. The thermogravimetric curves of palm kernel powder and polyvinyl chloride have showed that these materials lose their different masses in three different phases, and the one of composite (mixture of polyvinyl chloride with 12.5% of palm kernel powder) in two different phases.展开更多
It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport i...It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression(GPR), Support vector machine(SVM) and kernel extreme learning machine(KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions asinputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number(Fr), ratio of average velocity(V) to shear velocity(U*) and shields number(θ) yields a correlation coefficient(R) of 0.951, a Nash-Sutcliffe efficiency(NSE) of 0.903 and root mean squared error(RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.展开更多
Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a rea...Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models.展开更多
As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this p...As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching.展开更多
文摘This work presents the physical and thermal characterization of the dura palm kernel powder of Cameroon for their use as fillers for polymers composites. The powders of palm kernel were obtained using a percussion grinder mill with an industrial microniser which allowed obtaining a powder less than 50 μm with an apparent density between 0,505 ≤ ρ ≤ 0,680 g/cm3 at 1.56 of relative humidity. The infrared of the powder of palm kernel shows the presence of phenols groups with a large band around 3341 cm-1, -C-H at 2917.02 cm-1 and -C-O at 1040 cm-1 as the main peaks. The polyvinyl chloride of infrared obtained shows the presence of -C-Cl, -CH2 and CH as the mains peaks. The infrared of 12.5% of palm kernel powder with polyvinyl chloride shows an increase of the CH2 and CH bonds and a decrease of the -OH bonds. Thermogravimetric analysis and differential scanning calorimetric analysis of powders, polyvinyl chloride and mixture showed that the mixing powders are intermediate between the polyvinyl chloride and palm kernel powder. The powder decreased the phase temperatures of the mixture from 98.58℃ to 95℃ for the glass transition temperature and from 515℃ to 459℃ for the crystallization temperature. The thermogravimetric curves of palm kernel powder and polyvinyl chloride have showed that these materials lose their different masses in three different phases, and the one of composite (mixture of polyvinyl chloride with 12.5% of palm kernel powder) in two different phases.
文摘It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression(GPR), Support vector machine(SVM) and kernel extreme learning machine(KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions asinputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number(Fr), ratio of average velocity(V) to shear velocity(U*) and shields number(θ) yields a correlation coefficient(R) of 0.951, a Nash-Sutcliffe efficiency(NSE) of 0.903 and root mean squared error(RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.
文摘Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models.
基金Sponsored by the National 11th 5-year Plan Key Project of Ministry of Science and Technology of China (Grant No.2006BAJ01A04)
文摘As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching.
