The effective radio resource allocation al-gorithms, which satisfy diversiform requirements of mobile naltimedia services in wireless cellular net-works, have recently attracted more and more at-tention. This paper pr...The effective radio resource allocation al-gorithms, which satisfy diversiform requirements of mobile naltimedia services in wireless cellular net-works, have recently attracted more and more at-tention. This paper proposes a service-aware scheduling algorithm, in which the Mean Opinion Score （MOS） is chosen as the unified metric of the Quality of Experience （QoE）. As the network needs to provide satisfactory services to all the users, the fairness of QoE should be considered. The Propor- tional Fair （PF） principle is adopted to achieve the trade-off between the network perfonmnce and us- er fairness. Then, an integer progranming problem is formed and the QoE-aware PF scheduling princi-ple is derived by solving the relaxed problem. Simu-lation results show that the proposed scheduling principle can perform better in terms of user fair-ness than the previous principle maximizing the sum of MOS. It also outperfoms the max-rain scheduling principle in terms of average MOS.展开更多
Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, cr...Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.展开更多
Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neur...Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.展开更多
基金This paper was supported partially by the Program for New Century Excellent Talents in University under Crant No. NCET-11-0600 the National Natural Science Foundation of China under Crant NN76022 and the France Telecom R ＆ D Beijing Co. Ltd.
文摘The effective radio resource allocation al-gorithms, which satisfy diversiform requirements of mobile naltimedia services in wireless cellular net-works, have recently attracted more and more at-tention. This paper proposes a service-aware scheduling algorithm, in which the Mean Opinion Score （MOS） is chosen as the unified metric of the Quality of Experience （QoE）. As the network needs to provide satisfactory services to all the users, the fairness of QoE should be considered. The Propor- tional Fair （PF） principle is adopted to achieve the trade-off between the network perfonmnce and us- er fairness. Then, an integer progranming problem is formed and the QoE-aware PF scheduling princi-ple is derived by solving the relaxed problem. Simu-lation results show that the proposed scheduling principle can perform better in terms of user fair-ness than the previous principle maximizing the sum of MOS. It also outperfoms the max-rain scheduling principle in terms of average MOS.
文摘Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.
文摘Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.
