We investigate the characteristics of the transition from laminar to turbulent flow in the microtube with a diameter of 310μm. The microscopic particle image velocimetry is used to measure the water flow at Re =1600-...We investigate the characteristics of the transition from laminar to turbulent flow in the microtube with a diameter of 310μm. The microscopic particle image velocimetry is used to measure the water flow at Re =1600-2500 in the microtube. It is found that the flow transition occurs at Re=1600-1900, and the streamwise streaks and vortices appear in the transitional flow fields. These experimental observations provide a validation for the theoretical prediction of unstable travelling waves in pipe flow.展开更多
Intersubband linear and third-order nonlinear optical properties of conical quantum dots with infinite barrier potential are studied. The electronic structure of conical quantum dots through effective mass approximati...Intersubband linear and third-order nonlinear optical properties of conical quantum dots with infinite barrier potential are studied. The electronic structure of conical quantum dots through effective mass approximation is determined analytically. Linear, nonlinear, and total absorption coefficients, as well as the refractive indices of GaAs conical dots, are calculated. The effects of the size of the dots and of the incident electromagnetic field are investigated. Results show that the total absorption coefficient and the refractive index of the dots largely depend on the size of the dots and on the intensity and polarization of the incident electromaenetic field.展开更多
In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independ...In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.展开更多
基金Supported by the National Natural Science Foundation of China with Grant No 10272066.
文摘We investigate the characteristics of the transition from laminar to turbulent flow in the microtube with a diameter of 310μm. The microscopic particle image velocimetry is used to measure the water flow at Re =1600-2500 in the microtube. It is found that the flow transition occurs at Re=1600-1900, and the streamwise streaks and vortices appear in the transitional flow fields. These experimental observations provide a validation for the theoretical prediction of unstable travelling waves in pipe flow.
文摘Intersubband linear and third-order nonlinear optical properties of conical quantum dots with infinite barrier potential are studied. The electronic structure of conical quantum dots through effective mass approximation is determined analytically. Linear, nonlinear, and total absorption coefficients, as well as the refractive indices of GaAs conical dots, are calculated. The effects of the size of the dots and of the incident electromagnetic field are investigated. Results show that the total absorption coefficient and the refractive index of the dots largely depend on the size of the dots and on the intensity and polarization of the incident electromaenetic field.
文摘In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.
