Availability of digital elevation models （DEMs） of a high quality is becoming more and more important in spatial studies. Standard methods for DEM creation use only intentionally acquired data sources. Two approache...Availability of digital elevation models （DEMs） of a high quality is becoming more and more important in spatial studies. Standard methods for DEM creation use only intentionally acquired data sources. Two approaches which employ various types of data sets for DEM production are proposed： （1） Method of weighted sum of different data sources with morphological enhancement that conflates any additional data sources to principal DEM, and （2） DEM updating methods of modeling absolute and relative temporal changes, considering landslides, earthquakes, quarries, watererosion, building and highway constructions, etc. Spatial modeling of environmental variables concerning both approaches for （a） quality control of data sources, considering regions, （b） pre-processing of data sources, and （c） processing of the final DEM, have been applied. The variables are called rate of karst, morphologic roughness （modeled from slope, profile curvature and elevation）, characteristic features, rate of forestation, hydrological network, and rate of urbanization. Only the variables evidenced as significant were used in spatial modeling to generate homogeneous regions in spatial modeling a-c. The production process uses different regions to define high quality conflation of data sources to the final DEM. The methodology had been confirmed by case studies. The result is an overall high quality DEM with various well-known parameters.展开更多
A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to el...A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.展开更多
文摘Availability of digital elevation models （DEMs） of a high quality is becoming more and more important in spatial studies. Standard methods for DEM creation use only intentionally acquired data sources. Two approaches which employ various types of data sets for DEM production are proposed： （1） Method of weighted sum of different data sources with morphological enhancement that conflates any additional data sources to principal DEM, and （2） DEM updating methods of modeling absolute and relative temporal changes, considering landslides, earthquakes, quarries, watererosion, building and highway constructions, etc. Spatial modeling of environmental variables concerning both approaches for （a） quality control of data sources, considering regions, （b） pre-processing of data sources, and （c） processing of the final DEM, have been applied. The variables are called rate of karst, morphologic roughness （modeled from slope, profile curvature and elevation）, characteristic features, rate of forestation, hydrological network, and rate of urbanization. Only the variables evidenced as significant were used in spatial modeling to generate homogeneous regions in spatial modeling a-c. The production process uses different regions to define high quality conflation of data sources to the final DEM. The methodology had been confirmed by case studies. The result is an overall high quality DEM with various well-known parameters.
基金Supported in part by Chinese Recruitment Program of Global Young Expert,Alexander von Humboldt Research Fellowship of Germany,the Foundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (61074020)
文摘A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
