In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
In our previous paper we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids, using the speed of sound data. This is a continuation for evaluating TM EOS in predicting PVT properties of heavy n...In our previous paper we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids, using the speed of sound data. This is a continuation for evaluating TM EOS in predicting PVT properties of heavy n-alkanes. Liquid density of long-chain n-alkane systems from C 9 to C 20 have been calculated using an analytical equation of state based on the statistical-mechanical perturbation theory. The second virial coefficients of these n-alkanes are scarce and there is no accurate potential energy function for their theoretical calculation. In this work the second virial coefficients are calculated using a corresponding state correlation based on surface tension and liquid density at the freezing point. The deviation of calculated densities of these alkanes is within 0.5% from experimental data. The densities of n-alkanes obtained from the TM EOS are compared with those calculated from Ihm-Song-Mason equation of state and the corresponding-states liquid densities (COSTALD). Our results are in favor of the preference of the TM EOS over other two equations of state.展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金H. Karimi and F. Yousefi would like to thank Yasouj University for supporting this project
文摘In our previous paper we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids, using the speed of sound data. This is a continuation for evaluating TM EOS in predicting PVT properties of heavy n-alkanes. Liquid density of long-chain n-alkane systems from C 9 to C 20 have been calculated using an analytical equation of state based on the statistical-mechanical perturbation theory. The second virial coefficients of these n-alkanes are scarce and there is no accurate potential energy function for their theoretical calculation. In this work the second virial coefficients are calculated using a corresponding state correlation based on surface tension and liquid density at the freezing point. The deviation of calculated densities of these alkanes is within 0.5% from experimental data. The densities of n-alkanes obtained from the TM EOS are compared with those calculated from Ihm-Song-Mason equation of state and the corresponding-states liquid densities (COSTALD). Our results are in favor of the preference of the TM EOS over other two equations of state.
