在神经工程研究领域,神经信号模拟器用于验证神经解码算法和脑机接口系统的性能,设计实现一种基于FPGA和上位机的128通道神经信号模拟器。上位机负责生成神经信号和常用典型信号(如正弦波、三角波、方波)数据集,通过USB接口发送给下位...在神经工程研究领域,神经信号模拟器用于验证神经解码算法和脑机接口系统的性能,设计实现一种基于FPGA和上位机的128通道神经信号模拟器。上位机负责生成神经信号和常用典型信号(如正弦波、三角波、方波)数据集,通过USB接口发送给下位机转换成模拟信号输出。其中,USB实际通信速率需大于7.68 MB/s,使用USB控制器芯片CY7C68013A结合FPGA实现这一功能。下位机最终实现的基本参数如下:128通道、30 k SPS采样率、±10 m V幅值输出范围、12位DAC分辨率。使用业内应用最广泛的神经信号采集系统,对模拟器产生的信号进行分析,得到采集设备与模拟器的整体信噪比为75 d B。该模拟器具有可编辑性强、通道数易拓展、噪声小等优点,可应用于神经信号采集系统的性能测试、神经科学研究、脑机接口等领域。展开更多
The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorit...The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.展开更多
By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of ...By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.展开更多
The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblie...The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.展开更多
The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This a...The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.展开更多
A one-dimensional BOD-DO coupling model for water quality simulation is presented, which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network. The water quality data of Yangtze...A one-dimensional BOD-DO coupling model for water quality simulation is presented, which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network. The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model. The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.展开更多
文摘在神经工程研究领域,神经信号模拟器用于验证神经解码算法和脑机接口系统的性能,设计实现一种基于FPGA和上位机的128通道神经信号模拟器。上位机负责生成神经信号和常用典型信号(如正弦波、三角波、方波)数据集,通过USB接口发送给下位机转换成模拟信号输出。其中,USB实际通信速率需大于7.68 MB/s,使用USB控制器芯片CY7C68013A结合FPGA实现这一功能。下位机最终实现的基本参数如下:128通道、30 k SPS采样率、±10 m V幅值输出范围、12位DAC分辨率。使用业内应用最广泛的神经信号采集系统,对模拟器产生的信号进行分析,得到采集设备与模拟器的整体信噪比为75 d B。该模拟器具有可编辑性强、通道数易拓展、噪声小等优点,可应用于神经信号采集系统的性能测试、神经科学研究、脑机接口等领域。
文摘The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.
文摘By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.
文摘The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.
基金project BK2001073 supported by Jiangsu Province Natural Science Foundation
文摘The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.
基金Funded by the National Natural Science Foundation of China (No.59838300 No.59778021)
文摘A one-dimensional BOD-DO coupling model for water quality simulation is presented, which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network. The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model. The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.
