Focusing on the strategic goal of "Safe China", we will build a technical system for modernizing the theory and governance capacity of a smart society, and solve the problems of lagging theoretical innovation in resolving multiple conflicts and disputes, insufficient systematization of intelligent auxiliary technology for risk control and management, and poor coordination and linkage of governance. Enhance the ability to prevent and resolve major systemic risks, enhance scientific supervision and service capabilities, and provide scientific and technological support to promote the modernization of social governance system and governance capacity and build a smart society. Need to build a platform for analysis and control of conflicts and disputes and evaluation of their resolution effects. The platform aims to address the main problems in the resolution of multiple conflicts, build assessment indexes for the impact of conflicts and disputes, design intervention norms, multiple resolution mechanisms and policy tools synergistic model, build a large- scale dynamic knowledge map in the field of conflicts and disputes integrating the characteristics of the times, build an intelligent platform for the whole process of conflicts and disputes disposal, build a platform for psychological risk assessment and early warning, psychological assistance and automated psychological intervention, build The platform for analysis and control of contradictions and disputes and evaluation of the effectiveness of their resolution will be demonstrated and applied.
By introducing the object cloud into topological space, the spatial relationships between fuzzy objects transform to cloud
relationships in cloud space. According to cloud theory, all the spatial objects can be represented by three types object
cloud: point-cloud, line-cloud and area-cloud. So the 9-intersection model of spatial topological relations proposed by
Egenhofer can be extended by using the new definition of object cloud. The relationship between object clouds is
flexible relationship. Different from the crisp relationship model, 9IM, the flexible relationship model by object cloud
can be simplified to 4-intersection cloud model(4ICM), including to equal, contain, intersect and disjoint. The cloud
operation and virtue cloud can be introduced to representing the fuzzy and uncertain topological relations. The method
makes spatial data model enable to model the spatial phenomena with fuzziness and uncertainties, and enriches the cloud theory.
This paper presents a new method applied to texture feature representation in RS image based on cloud model. Aiming at
the fuzziness and randomness of RS image, we introduce the cloud theory into RS image processing in a creative way.
The digital characteristics of clouds well integrate the fuzziness and randomness of linguistic terms in a unified way and
map the quantitative and qualitative concepts. We adopt texture multi-dimensions cloud to accomplish vagueness and
randomness handling of texture feature in RS image. The method has two steps: 1) Correlativity analyzing of texture
statistical parameters in Grey Level Co-occurrence Matrix (GLCM) and parameters fuzzification. GLCM can be used to
representing the texture feature in many aspects perfectly. According to the expressive force of texture statistical
parameters and by Correlativity analyzing of texture statistical parameters, we can abstract a few texture statistical
parameters that can best represent the texture feature. By the fuzziness algorithm, the texture statistical parameters can be
mapped to fuzzy cloud space. 2) Texture multi-dimensions cloud model constructing. Based on the abstracted texture
statistical parameters and fuzziness cloud space, texture multi-dimensions cloud model can be constructed in micro-windows
of image. According to the membership of texture statistical parameters, we can achieve the samples of cloud-drop.
By backward cloud generator, the digital characteristics of texture multi-dimensions cloud model can be achieved
and the Mathematical Expected Hyper Surface(MEHS) of multi-dimensions cloud of micro-windows can be constructed.
At last, the weighted sum of the 3 digital characteristics of micro-window cloud model was proposed and used in texture
representing in RS image. The method we develop is demonstrated by applying it to texture representing in many RS
images, various performance studies testify that the method is both efficient and effective. It enriches the cloud theory,
and proposes a new idea for image texture representing and analyzing, especially RS image.
A fuzzy edge detection algorithm based on object-cloud and maximum fuzzy entropy principle are proposed in this
paper. According to the uncertainty of the objects in the RS image, the spatial objects in RS image space can be mapped
to the cloud space by 1:M cloud model. Object-cloud will have the digital characteristics to describe the fuzziness and
randomicity of objects in RS image. According to the cloud operation, boundary-cloud and its digital characteristics can
be achieved and the membership matrix of transition region can be constructed. By maximum fuzzy entropy principle,
edge detection can be accomplished in the membership matrix of transition region.
Based on gray and texture features of remote sensing (RS) image, a new method of textural combined association rules
mining is proposed in this paper. According to the spectrum features of pixels of image, all the pixels constructing the
textural RS image and all the texture cells have relationships between each other. This is premise of mining association
rules in image. In order to mine the textural association rules in RS image, each image can be seen one transaction, and
frequent patterns can be mined. If image data mining drills down to pixel level, each pixel or its neighborhood can be
seen one transaction too, and data mining was processed in all the transactions. In textural image, the frequent patterns
are texture cells in fact. Because of different size of texture cells, multi-levels and multi-masks data mining was studied.
Based on definition of image association rules, one association rule represents the local structure of RS image, and the
support s% and confidence c% denote the possibility of the pattern. The experimental results validate that the combined
association rules can represent the regular texture, and can represent the irregular texture perfectly too. By the combined
association rules we can accomplish image segmentation.
The spatial region in RS image has positional and thematic values uncertainties. Based on the uncertainties and the cloud
theory, the paper studies the representation of spatial uncertain region in image and proposes a new method applied to
spatial uncertain region representation based on cloud model. In 2-dimensional universe of discourse, by the gray and gradient or other digital characters of image, we can construct object-cloud of spatial object. Uncertain spatial region can be represented by object-cloud. Edge of spatial object can be represented by half-cloud-ring. So spatial uncertain region can be represented based on cloud model properly. Experiments testify that the method is both efficient and effective. It enriches the cloud theory, and proposes a new idea for representation of fuzzy object and image comprehending and analyzing, especially remote sensing image.
Fuzzy edge is the essential character of image and the main obstacle for image comprehension. That is why the fuzzy mathematics applied to edge detection widely. But most of fuzzy algorithms could not express the fuzziness and random of edge of image precisely. Edge information with lower gray-level was lost, and the result lacks precision. Cloud model is a flexible and useful model for treating uncertain issues. It considers fuzziness and random at the same time, overcomes the limitation of classical fuzzy algorithms. The paper proposes Object-Cloud Algorithm (OCA). In OCA, we consider that objects in image are fuzzy objects with uncertain edge. The degree membership of each pixel belonging to an object is inverse proportion with the distance between pixel and center of the object. So each object can be represented with a cloud, which can be called "object-cloud". Based on "object-cloud", OCA replaces microcosmic pixels with macroscopical objects. Pixel is just element of "object-cloud", and edge information with lower gray-level can be preserved precisely. OCA has two steps. First, creating a cloud for each object in image. Uncertain point and uncertain polyline in image can be treated as uncertain area. Area-cloud includes "core" and "half-cloud loop" outside the core. It can be represented by model A(Area,En,He) based on cloud theory. En is entropy. He is hyperentropy. Generating core (Area) is the precondition of creating an uncertain area-cloud. Second, achieving the fuzzy edge of object in image. After creating "object-cloud" for each object in image, adjacent area can be represented by intersecting clouds. We can gain a new "boundary-cloud" by Boolean calculation between two or more intersecting clouds which have public boundary. The fuzzy edge between objects in image can be detected by digital characteristics of "boundary-cloud" and cloud calculation.
Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. Clustering algorithms typically use the Euclidean distance. In the real world, there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. In this paper, we study the problem of clustering in the presence of obstacles and propose spatial clustering by Voronoi distance in Voronoi diagram (Thiessen polygon). Voronoi diagram has lateral spatial adjacency character. Based on it, we can express the spatial lateral adjacency relation conveniently and solve the problem derived from spatial clustering in the presence of obstacles. The method has three steps. First, building the Voronoi diagram in the presence of obstacles. Second, defining the Voronoi distance. Based on Voronoi diagram, we propose the Voronoi distance. Giving two spatial objects, Pi and Pj, The Voronoi distance is defined that the minimum object Voronoi regions number between Pi and Pj in the Voronoi diagram. Third, we propose Following-Obstacle-Algorithm (FOA). FOA includes three steps: the initializing step, the querying step and the pruning step. By FOA, we can get the Voronoi distance between any two objects. By Voronoi diagram and the FOA, the spatial clustering in the presence of obstacles can be accomplished conveniently, and more precisely. We conduct various performance studies to show that the method is both efficient and effective.
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