为了对大规模的数据访问和海量海洋信息的处理提供可靠实时的云计算服务,结合工作流与软件即服务(software-as-a-service,SaaS)的思想,提出软件服务流的概念,并构建基于云平台的软件服务流体系结构的系统.服务流引擎在整个系统中处于底层,与Hadoop平台进行交互,运行自行设计的服务流解析与重组算法处理用户请求,并交付下层执行,且为上层提供资源表述性转移(representational state transfer,REST)架构风格的服务流监控和资源管理的透明接口,降低了开发的复杂性,提高系统的可伸缩性.用户能够通过Web端访问,定制个性化软件服务,并且能实时监控云平台.在该平台上,大规模数据访问、高并发以及高密度的访问也是一种常态.通过构建初步的原型系统,证明平台体系结构的可用性和高效性.
The optical flow analysis of the image sequence based on the formal lattice Boltzmann equation, with different DdQm models, is discussed in this paper. The Mgorithm is based on the lattice Boltzmann method （LBM）, which is used in computational fluid dynamics theory for the simulation of fluid dynamics. At first, a generalized approximation to the formal lattice Boltzmann equation is discussed. Then the effects of different DdQm models on the results of the optical flow estimation are compared with each other, while calculating the movement vectors of pixels in the image sequence. The experimental results show that the higher dimension DdQm models, e.g., D3Q15 are more effective than those lower dimension ones.
In remote sensing sea surface temperature （SST）, the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validation by field measurement. However, field measurement that relative to the satellite measurement is very sparse, many information may not be verified. A relative objective weight vector is constructed by using the limited field measurement, which is based on coefficient of variation method. And then it make an application of the data fusion by the weighted average method in the SST data. fuse SST data with the weighted average method. In this way, some posteriori information can be added to the fusion process. The model reduces the dependence on verification, and some of the satellite measurement can be handled without corresponding to the field measurement, and the fusion result matches transfer errors theory.
