Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditi...To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.展开更多
The flotation of kaolinite using a series of tertiary amines （N,N-dimethyl-dodecyl amine （DRN）, N,N-diethyl-dodecyl amine （DEN）, N,N-dipropyl-dodecyl amine （DPN） and N,N-dibenzyl-bodecyl amine （DBN）） was inv...The flotation of kaolinite using a series of tertiary amines （N,N-dimethyl-dodecyl amine （DRN）, N,N-diethyl-dodecyl amine （DEN）, N,N-dipropyl-dodecyl amine （DPN） and N,N-dibenzyl-bodecyl amine （DBN）） was investigated. The results show that the maximum recoveries of kaolinite for DEN, DPN and DRN are 93%, 88% and 84%, respectively, but that of DBN is very low. On the basis of zeta potential and FT-IR spectra, the ionization of surface hydroxyl and isomorphic exchange of surface ions account for the charging mechanisms of kaolinite surface. The adsorption mechanism of tertiary amines on kaolinite surface is mainly electrostatic. The isoelectric point （IEP） of kaolinite increases from 3.4 to some more positive points after the interaction of kaolinite with the four tertiary amines. The FT-IR spectra of kaolinite change with the presence of some new sharp shapes belonging to the tertiary amines. The inductive electronic effects and space-steric effects of -CH3, -C2H5, -C3H7 and -C7H7 bonding to N atom result in different collecting power of the four tertiary amines.展开更多
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
基金The National Natural Science Foundation of China (No.71771019, 71871130, 71971125)the Science and Technology Special Project of Shandong Provincial Public Security Department (No. 37000000015900920210010001,37000000015900920210012001)。
文摘To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.
基金Project(2005CB623701) supported by the National Basic Research Program of China
文摘The flotation of kaolinite using a series of tertiary amines （N,N-dimethyl-dodecyl amine （DRN）, N,N-diethyl-dodecyl amine （DEN）, N,N-dipropyl-dodecyl amine （DPN） and N,N-dibenzyl-bodecyl amine （DBN）） was investigated. The results show that the maximum recoveries of kaolinite for DEN, DPN and DRN are 93%, 88% and 84%, respectively, but that of DBN is very low. On the basis of zeta potential and FT-IR spectra, the ionization of surface hydroxyl and isomorphic exchange of surface ions account for the charging mechanisms of kaolinite surface. The adsorption mechanism of tertiary amines on kaolinite surface is mainly electrostatic. The isoelectric point （IEP） of kaolinite increases from 3.4 to some more positive points after the interaction of kaolinite with the four tertiary amines. The FT-IR spectra of kaolinite change with the presence of some new sharp shapes belonging to the tertiary amines. The inductive electronic effects and space-steric effects of -CH3, -C2H5, -C3H7 and -C7H7 bonding to N atom result in different collecting power of the four tertiary amines.
