In order to visualize spatio-temporal data and temporal geographic information in a dynamic way, a spatio-temporal GIS that are
capable of demonstrating how geographic phenomena evolving should be established, for the fundamental theory of spatio-temporal
GIS, it is urgent to analyze the impetus and mechanism that how spatio-temporal process happened. The authors proposed a set of
universal change patterns for modelling of spatio-temporal processes, which builds a fundamental basis for the representation of
dynamic phenomena. Change detection algorithms were also developed and pattern association methods were implemented for the
modelling of changing geographical world.
Traditional modeling methods on spatial objects are not eligible to deal well with the fuzzy features that acquired from
image, some research need to be carried out on the fuzzy spatial objects modeling. With the deep investigation on the
spatial objects model of GIS and the representation of natural geographical feature, fuzzy spatial objects have been
proposed by researchers. Referring to the characteristics of the representation of fuzzy spatial objects, a generation
method of fuzzy spatial objects based on fuzzy Neural Networks is going to be demonstrated by the authors in this paper.
By combining the fuzzy technique and neural networks, utilizing the learning ability to enhance the fuzzy membership
function and fuzzy rules, the system will be self-Adaptive. By comparing with the traditional fuzzy objects generation,
the method in this paper improves the accuracy of results according to the experiments in this paper.
In order to develop a quality assurance system for maps thus obtained from the National Western Surveying and Mapping Project on 1:50000 Topological Maps Blank Area, a spatial data quality check method based on entity and evaluation method by implementing the cloud theory and rough set is going to be put forward in the paper. First, spatial data quality problems are to be analyzed and possible quality problems will be described. Secondly, a digital linear graphic spatial data quality model is built and the quality elements and sub-elements are elaborately summarized. Next, spatial computing operators that the check process demands are given. The weight of each index is calculated according to importance of attribute in rough set. Cloud decision generator transforms indexes value into qualitative evaluation. Finally, the homologous software of the spatial data quality check and evaluation is developed to control spatial data quality. Therefore a spatial data quality control and evaluation technique system is founded. It shows that the check and
evaluation methods are feasible and software has higher automation from the experiment.
In an inertial confinement fusion (ICF) system, wave-front aberrations existed in laser beam will enlarge the focal spot
size and decrease power density at the target. Fortunately, an adaptive optical system (AO) could be employed in ICF
system to correct the beam aberrations. As a powerful wave-front detector, Hartmann-Shack (H-S) sensor is often
utilized as a wave-front sensor in AO. However, H-S sensor can not detect the aberrations after the sampling location. A
new method is presented to measure the aberrations of entire ICF beam path in this paper. Based on the AO, a CCD is
installed in the target chamber to detect the focal spot distribution. The deformable mirror's (DM) is yielded to different
surface shapes; the extra different aberrations are modulated and added to ICF beam path, and then create their
corresponding focal spots. The extra aberrations and the corresponding focal spots intensity could be recorded
simultaneously by H-S sensor and CCD respectively. An amendatory phase-retrieval algorithm which is introduced can
reconstruct the aberrations of entire ICF beam path from the pairs of extra aberrations and their corresponding focal spots
intensity. The numerical simulation show that the AO can correct the aberrations of entire beam path of ICF successfully
based on this method.