Grid classification

Abstract: The paper develops the use of Meshless grid systems. The grid classification used in this paper is described as a "point-structured" grid as it uses a mapping relationship to establish node connections. This means a point-structur ed grid can be used to classify all other grid-types demonstrating a strong generality. The mapping relationship is used to undertake the discretization procedure. The mapping relationship is formally introduced and discussed. The classification could be considered as the same as a Meshless grid system. To demonstrate the point-structured classification a new type of grid system, called a "Crisscross grid", is presented as an example. The Crisscross grid cannot be classified in terms of either structured or unstructured grid systems. Solutions for Laplace's equation and the convection- diffusion equation are found and presented. The method of discretizatio n applied in the present study uses a polynomial spatial-function fitted over the computational molecule. To prevent oscillations in the spatial solution of the convection-diffusion equation a crude technique that adds artificial viscosity is employed.

Abstract: As a new means of data acquisition, vehicle-borne laser scanning technology can quickly obtain accurate the three-dimensional spatial information of objects, it is very suitable for urban objects in the rapid access of three-dimensional spatial information. The colored point cloud data by fusing laser data and CCD image, not only having spatial information, but also the color attribute information. In this paper, colored point cloud data is the object of the study, the author proposed a strategy of stepwise classification: projection grid classification combine with supervised classification. Firstly, all the data points are projected on the horizontal grid, according to the properties of grid cells information, they are divided into three categories: ground class, building class, and other object class. Secondly, for each category, using the method of supervised classification, according to spectral information (RGB), dividing the points into different classes in order to achieve the classification and extraction of surface features. Experimental results show that different kinds of objects can be extracted and classified effectively using the method.

Abstract: In this paper, we present a new approach to the parallelization of algebraic multigrid (AMG), i.e. to the parallel coarse-grid selection in AMG. Our approach involves (almost) no special treatment of processor subdomain boundaries and hence avoids a number of drawbacks of other AMG parallelization techniques. The key idea is to select an appropriate (local) coarse grid on each processor from a set of admissible grids such that the composed coarse grid forms a suitable coarse grid for the whole domain, i.e. there is no need for a special boundary treatment. To this end, we first construct multiple equivalent coarse grids on each processor subdomain. In a second step we then select exactly one grid per processor by a graph clustering technique. The results of our numerical experiments clearly indicate that this approach results in coarse grids of high quality which are very close to those obtained with sequential AMG. Furthermore, the operator and grid complexities of our parallel AMG are mostly smaller than those obtained by other parallel AMG methods, whereas the scale-up behaviour of the proposed algorithm is similar to that of other parallel AMG techniques. However a significant improvement with respect to the speed-up performance is achieved. Copyright © 2006 John Wiley & Sons, Ltd.