In order to predict and control the properties of Cu-Cr-Sn-Zn alloy,a model of aging processes via an artificial neural network(ANN) method to map the non-linear relationship between parameters of aging process and th...In order to predict and control the properties of Cu-Cr-Sn-Zn alloy,a model of aging processes via an artificial neural network(ANN) method to map the non-linear relationship between parameters of aging process and the hardness and electrical conductivity properties of the Cu-Cr-Sn-Zn alloy was set up.The results show that the ANN model is a very useful and accurate tool for the property analysis and prediction of aging Cu-Cr-Sn-Zn alloy.Aged at 470-510 ℃ for 4-1 h,the optimal combinations of hardness 110-117(HV) and electrical conductivity 40.6-37.7 S/m are available respectively.展开更多
Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around t...Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.展开更多
It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous s...It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.展开更多
Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a ...Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a fan system is con-cerned. Basically, a fan is mainly consisting of a single phase induction motor and therefore fan system parameters are essentially the electrical parameters e.g. resistances, reactances and some load parameters (fan blades).These parame-ters often change under varying operating conditions and the knowledge of these parameters is necessary to have opti-mum and efficient operation of the system. Therefore, fan system parameters are required to be estimated. Further, fan system parameters estimation is required to ensure the smooth system operation and to avoid any malfunctioning of the system during abnormal working conditions. In this paper, Artificial Neural Networks (ANN) approach has been used for parameter estimation of a fan system. The simulated and experimental results are compared.展开更多
Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power syst...Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.展开更多
基金Project(2006AA03Z528) supported by the National High-Tech Research and Development Program of ChinaProject(102102210174) supported by the Science and Technology Research Project of Henan Province,ChinaProject(2008ZDYY005) supported by Special Fund for Important Forepart Research in Henan University of Science and Technology
文摘In order to predict and control the properties of Cu-Cr-Sn-Zn alloy,a model of aging processes via an artificial neural network(ANN) method to map the non-linear relationship between parameters of aging process and the hardness and electrical conductivity properties of the Cu-Cr-Sn-Zn alloy was set up.The results show that the ANN model is a very useful and accurate tool for the property analysis and prediction of aging Cu-Cr-Sn-Zn alloy.Aged at 470-510 ℃ for 4-1 h,the optimal combinations of hardness 110-117(HV) and electrical conductivity 40.6-37.7 S/m are available respectively.
文摘Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University(KKU)for funding this research project Number(R.G.P.2/133/43).
文摘It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.
文摘Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a fan system is con-cerned. Basically, a fan is mainly consisting of a single phase induction motor and therefore fan system parameters are essentially the electrical parameters e.g. resistances, reactances and some load parameters (fan blades).These parame-ters often change under varying operating conditions and the knowledge of these parameters is necessary to have opti-mum and efficient operation of the system. Therefore, fan system parameters are required to be estimated. Further, fan system parameters estimation is required to ensure the smooth system operation and to avoid any malfunctioning of the system during abnormal working conditions. In this paper, Artificial Neural Networks (ANN) approach has been used for parameter estimation of a fan system. The simulated and experimental results are compared.
基金supported by the Electric Power Research Institute and also makes use of Engineering Research Center Shared Facilities supported by the DOE under U.S.NSF Award Number EEC1041877support is provided by the U.S.CURENT Industry Partnership Program and China National Government Building Highlevel University Graduate Programs([2012]3013).
文摘Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.
