Parameters Inversion of Fluid-Saturated Porous Media Based on Neural Networks

The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

A Novel Counter Sheet-Flow Sandwich Cell Culture Device For Mammalian Cell Growth In Space

Cell culture and growth in space is crucial to understand the cellular responses under microgravity. The effects of microgravity were coupled with such environment restrictions as medium perfusion, in which the underlying mechanism has been poorly understood. In the present work, a customer-made counter sheet-flow sandwich cell culture device was developed upon a biomechanical concept from fish gill breathing. The sandwich culture unit consists of two side chambers where the medium flow is counter-directional, a central chamber where the cells are cultured, and two porous polycarbonate membranes between side and central chambers. Flow dynamics analysis revealed the symmetrical velocity profile and uniform low shear rate distribution of flowing medium inside the central culture chamber, which promotes sufficient mass transport and nutrient supply for mammalian cell growth. An on-orbit experiment performed on a recovery satellite was used to validate the availability of the device.