Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department t...Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.展开更多
The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Ra...The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.展开更多
Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） mod...Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） model was applied to evaluate relative efficiency of water resource allocation plans. This model determined weights of indicators by GEM, and assessed the allocation schemes by MAUE. Compared with DEA （Data Envelopment Analysis） or ANN （Artificial Neural Networks）, the mode was more applicable in some cases where decision-makers had preference for certain indicators展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
基金Project(61873283)supported by the National Natural Science Foundation of ChinaProject(KQ1707017)supported by the Changsha Science&Technology Project,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.
基金Project(51778626) supported by the National Natural Science Foundation of China
文摘The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.
文摘Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） model was applied to evaluate relative efficiency of water resource allocation plans. This model determined weights of indicators by GEM, and assessed the allocation schemes by MAUE. Compared with DEA （Data Envelopment Analysis） or ANN （Artificial Neural Networks）, the mode was more applicable in some cases where decision-makers had preference for certain indicators
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
