The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should ...The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should be beneficial to the complete integration of RS with GIS,able to deal with the disagreement between the resolution of remote sensing images and the precision of GIS data,and also helpful to the knowledge discovery and exploitation.In this paper,the database structure storing the spatial data based on semantic network is presented.This database structure has several advantages.Firstly,the spatial data is stored as raster data with space index,so the image processing can be done directly on the GIS data that is stored hierarchically according to the distinguishing precision.Secondly,the simple objects are aggregated into complex ones.Thirdly,because we use the indexing tree to depict the relationship of aggregation and the indexing pictures expressed by 2_D strings to describe the topology structure of the objects,the concepts of surrounding and region are expressed clearly and the semantic content of the landscape can be illustrated well.All the factors that affect the recognition of the objects are depicted in the factor space,which provides a uniform mathematical frame for the fusion of the semantic and non_semantic information.Lastly,the object node,knowledge node and the indexing node are integrated into one node.This feature enhances the ability of system in knowledge expressing,intelligent inference and association.The application shows that this database structure can benefit the interpretation of remote sensing image with the information of GIS.展开更多
According to the frequency property of Phasedarray ground penetrating radar (PGPR), this paper gives a frequency point slice method based on Wigner time-frequency analysis. This method solves the problem of analysis f...According to the frequency property of Phasedarray ground penetrating radar (PGPR), this paper gives a frequency point slice method based on Wigner time-frequency analysis. This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable. At last, the analytical results of road test data of the Three Gorges prove the analytical method efficient. Key words phased-array ground penetrating radar - wigner time-frequency analysis - superposition data - object identification CLC number TN 715.7 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and 863 Program Foundation of China (2001AA132050-03)Biography: ZOU Lian (1975-), male, Ph. D candidate, research direction: signal processing.展开更多
As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this prob...As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.展开更多
There are a number of dirty data in observation data set derived from integrated ocean observing network system.Thus,the data must be carefully and reasonably processed before they are used for forecasting or analysis...There are a number of dirty data in observation data set derived from integrated ocean observing network system.Thus,the data must be carefully and reasonably processed before they are used for forecasting or analysis.This paper proposes a data pre-processing model based on intelligent algorithms.Firstly,we introduce the integrated network platform of ocean observation.Next,the pre-processing model of data is presented,and an intelligent cleaning model of data is proposed.Based on fuzzy clustering,the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering.The proposed dynamic algorithm can automatically find the new clustering center with the updated sample data.The rapid and dynamic performance of the model makes it suitable for real time calculation,and the efficiency and accuracy of the model is proved by test results through observation data analysis.展开更多
Currently the indoor environment quality is described or evaluated mainly by the subjective or objective data.However,research increasingly has demonstrated that objective and subjective data both had some weaknesses ...Currently the indoor environment quality is described or evaluated mainly by the subjective or objective data.However,research increasingly has demonstrated that objective and subjective data both had some weaknesses to characterize the indoor environment quality,and they can compensate for each other's relative weaknesses.Hence,this study aims to develop an integration model to allow indoor subjective and objective data to be combined based on the structural equation modeling approach,using the Northeast China residential indoor environmental survey data.The results indicated that the integration model had a good fit for the survey data,and the model validity was confirmed.Moreover,in contrast to the subjective data(R^(2)=0.363)and objective data(R^(2)=0.239),the integrated data(R^(2)=0.553)improved the explanatory power on the satisfaction with the overall indoor environment.Furthermore,this integration model demonstrated that indoor subjective data assigned more weights to the integrated data than the corresponding objective data.The association strength of thermal environment and indoor air quality(0.43 or 0.47)was the strongest among the interactions of thermal,air quality,acoustic,and lighting environments.Consequently,the main contribution of this paper was that it provided a comprehensive model to accomplish the integration of indoor environmental subjective and objective data,promoting the ability to describe and assess the indoor environment quality.展开更多
基金Supported by National Basic Research Program of China（973 Program）（2013CB035500） National Natural Science Foundation of China（61233004,61221003,61074061）+1 种基金 International Cooperation Program of Shanghai Science and Technology Commission （12230709600） the Higher Education Research Fund for the Doctoral Program of China（20120073130006）
文摘The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should be beneficial to the complete integration of RS with GIS,able to deal with the disagreement between the resolution of remote sensing images and the precision of GIS data,and also helpful to the knowledge discovery and exploitation.In this paper,the database structure storing the spatial data based on semantic network is presented.This database structure has several advantages.Firstly,the spatial data is stored as raster data with space index,so the image processing can be done directly on the GIS data that is stored hierarchically according to the distinguishing precision.Secondly,the simple objects are aggregated into complex ones.Thirdly,because we use the indexing tree to depict the relationship of aggregation and the indexing pictures expressed by 2_D strings to describe the topology structure of the objects,the concepts of surrounding and region are expressed clearly and the semantic content of the landscape can be illustrated well.All the factors that affect the recognition of the objects are depicted in the factor space,which provides a uniform mathematical frame for the fusion of the semantic and non_semantic information.Lastly,the object node,knowledge node and the indexing node are integrated into one node.This feature enhances the ability of system in knowledge expressing,intelligent inference and association.The application shows that this database structure can benefit the interpretation of remote sensing image with the information of GIS.
文摘According to the frequency property of Phasedarray ground penetrating radar (PGPR), this paper gives a frequency point slice method based on Wigner time-frequency analysis. This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable. At last, the analytical results of road test data of the Three Gorges prove the analytical method efficient. Key words phased-array ground penetrating radar - wigner time-frequency analysis - superposition data - object identification CLC number TN 715.7 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and 863 Program Foundation of China (2001AA132050-03)Biography: ZOU Lian (1975-), male, Ph. D candidate, research direction: signal processing.
文摘As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.
基金Key Science and Technology Project of the Shanghai Committee of Science and Technology, China (No.06dz1200921)Major Basic Research Project of the Shanghai Committee of Science and Technology(No.08JC1400100)+1 种基金Shanghai Talent Developing Foundation, China(No.001)Specialized Foundation for Excellent Talent of Shanghai,China
文摘There are a number of dirty data in observation data set derived from integrated ocean observing network system.Thus,the data must be carefully and reasonably processed before they are used for forecasting or analysis.This paper proposes a data pre-processing model based on intelligent algorithms.Firstly,we introduce the integrated network platform of ocean observation.Next,the pre-processing model of data is presented,and an intelligent cleaning model of data is proposed.Based on fuzzy clustering,the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means clustering.The proposed dynamic algorithm can automatically find the new clustering center with the updated sample data.The rapid and dynamic performance of the model makes it suitable for real time calculation,and the efficiency and accuracy of the model is proved by test results through observation data analysis.
基金supported by the National Natural Science Foundation of China(No.51978121 and No.51578103)the Key Projects in the National Science&Technology Pillar Program during the 12th Five-year Plan Period of China(No.2012BAJ 02B05)the National Key R&D Program during the 13th Five-year Plan Period of China(No.2018YFD1100701).
文摘Currently the indoor environment quality is described or evaluated mainly by the subjective or objective data.However,research increasingly has demonstrated that objective and subjective data both had some weaknesses to characterize the indoor environment quality,and they can compensate for each other's relative weaknesses.Hence,this study aims to develop an integration model to allow indoor subjective and objective data to be combined based on the structural equation modeling approach,using the Northeast China residential indoor environmental survey data.The results indicated that the integration model had a good fit for the survey data,and the model validity was confirmed.Moreover,in contrast to the subjective data(R^(2)=0.363)and objective data(R^(2)=0.239),the integrated data(R^(2)=0.553)improved the explanatory power on the satisfaction with the overall indoor environment.Furthermore,this integration model demonstrated that indoor subjective data assigned more weights to the integrated data than the corresponding objective data.The association strength of thermal environment and indoor air quality(0.43 or 0.47)was the strongest among the interactions of thermal,air quality,acoustic,and lighting environments.Consequently,the main contribution of this paper was that it provided a comprehensive model to accomplish the integration of indoor environmental subjective and objective data,promoting the ability to describe and assess the indoor environment quality.
