The Experts below are selected from a list of 4365 Experts worldwide ranked by ideXlab platform
Ling Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Granularity-Based Routing Algorithm for Dynamic Networks
Journal of Networks, 2014Co-Authors: Yan Ping Zhang, Zhou Xiancun, Renjin Liu, Ling ZhangAbstract:When dynamic network exhibits extremely complex behavior and keeps on changing all the time, the energy efficiency is the most important key-point of routing algorithm. Many empirical measurements are inadequate to represent dynamic networks. However, the Quotient Space Theory is an in-depth treatment of hierarchical problem solving, and powerful abilities of representation with different granularities. In this paper, we present a novel approach based on Quotient Space Theory to reduce the computation complexity of routing algorithm in the dynamic network. Firstly, we analyze the structure of dynamic network and use community-based multi-granular representation to represent the network. Then we develop a routing algorithm based on multi-granular Spaces. Finally, we compare the proposed algorithm with several alternative methods and the results show that our algorithm clearly outperforms the comparison methods in the road network
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Quotient Space based problem solving a theoretical foundation of granular computing
2014Co-Authors: Ling Zhang, Bo ZhangAbstract:Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search. Explains the Theory of hierarchical problem solving, its computational complexity, and discusses the principle and applications of multi-granular computing Describes a human-like, theoretical framework using Quotient Space Theory, that will be of interest to researchers in artificial intelligence. Provides many applications and examples in the engineering and computer science area. Includes complete coverage of planning, heuristic search and coverage of strictly mathematical models.
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Automatic Spatial Planning
Quotient Space Based Problem Solving, 2014Co-Authors: Ling Zhang, Bo ZhangAbstract:Automatic spatial planning, i.e. automatic robot planning, is discussed as one of the applications of Quotient Space Theory. We pay attention to how the Theory is applied to the problem, and how multi-granular computing can reduce its computational complexity.
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The Expansion of Quotient Space Theory
Quotient Space Based Problem Solving, 2014Co-Authors: Ling Zhang, Bo ZhangAbstract:In this chapter, we extend the Quotient Space Theory to general cases. It includes two aspects. First, the falsity- and truth-preserving principles are extended to a general Quotient Space approximation principle. Second, the Quotient Space Theory based on equivalence relations is extended to that based on tolerant relations and closure operations.
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network performance analysis based on Quotient Space Theory
Granular Computing, 2013Co-Authors: Ling Zhang, Shu Zhao, Yuanting Yan, Yan Ping ZhangAbstract:Some performance analyses in complex network e.g., shortest path, etc. are complicated. Generally, human have natural ability to solve complex problems by approximating the optimal solution step by step. The granular computing model based on QST Quotient Space Theory provides not only a hierarchical description from fine to coarse but also an effective approach from coarse to fine to solve these complex problems. This paper proposes some methods on complex network performance analysis based on QST. Firstly, maximum cover network chain is used to solve the shortest path problem. Then, a method to find the optimal path of a weighted network is put forward. Finally, dynamic network is decomposed into a series of static networks to solve the maximum flow problem in dynamic network. Theoretical proofs and experimental results show that QST is an effective tool for complex problem solving.
Yan Ping Zhang - One of the best experts on this subject based on the ideXlab platform.
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relational granulation method based on Quotient Space Theory for maximum flow problem
Information Sciences, 2020Co-Authors: Shu Zhao, Yan Ping Zhang, Jie Chen, Xian Sun, Zhen Duan, Yiwen ZhangAbstract:Abstract Granular computing (GrC) is a problem-solving concept deeply rooted in human thinking. GrC, as a new and rapidly growing paradigm of information processing, has attracted the attention of many researchers and practitioners. GrC is related to granulation, i.e., a process of drawing a set of objects (or points) together based on their indiscernibility, similarity, proximity, or functionality. In general, two types of granulation processes exist: functional granulation and relational granulation. If the process is based entirely on the attributes of the objects, it is known as functional granulation, whereas if the granulation process is based on the relationship between objects, it is known as relational granulation. This paper proposes a novel method, called the maximum flow based on Quotient Space Theory ( M F − Q S T ), for solving the maximum flow problems based on Quotient Space Theory for relational granulation. Using the method M F − Q S T , substructures are first detected, and the community is described as a substructure. Next, the relational granulation criterion is discussed in detail. The substructure that satisfies the relational granulation criterion is regarded as a coarse-grained node. Subsequently, the construction of a Quotient network that is coarser than the original network is described. Finally, the maximum flow algorithm is used to compute the maximum flow on the Quotient network as the approximated maximum flow on the original network within a much shorter period of time. Experimental results demonstrate that the novel method M F − Q S T reduces the cumulative running time after simplifying the network structure with a low error rate. The size of the Quotient network is significantly reduced, and the node and edge scales are reduced to 20.59% and 21.62% on average, respectively.
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A multi-granular network representation learning method
Granular Computing, 2019Co-Authors: Jie Chen, Shu Zhao, Xian Sun, Du Ziwei, Yan Ping ZhangAbstract:Granular computing (GrC) as a problem-solving concept and new information processing paradigm is deeply rooted in human thinking, which has attracted many researchers to study it theoretically, and has gradually applied to data-driven problems. Network embedding, as known as network representation learning, aiming to map nodes in network into a low-dimensional representation, is a data-driven problem. Most existing methods are based on a single granular, which learn representations from local structure of nodes. But global structure is important information on the network and has been proven to facilitate several network analysis tasks. Therefore, how to introduce GrC into network embedding to obtain a multi-granular network representation that preserves the global and local structure of nodes is a meaningful and tough task. In this paper, we introduce Quotient Space Theory, one of the GrC theories into network embedding and propose a Multi-Granular Network Representation Learning method based on Quotient Space Theory (MG_NRL, for short), which can preserve global and local structure at different granularities. Firstly, we granulate the network repeatedly to obtain a multi-granular network. Secondly, the embedding of the coarsest network is computed using any existing embedding method. Finally, the network representation of each granular layer is learned by recursively refining method from the coarsest network to original network. Experimental results on multi-label classification task demonstrate that MG_NRL significantly outperforms other state-of-the-art methods.
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Multi-Granularity-Based Routing Algorithm for Dynamic Networks
Journal of Networks, 2014Co-Authors: Yan Ping Zhang, Zhou Xiancun, Renjin Liu, Ling ZhangAbstract:When dynamic network exhibits extremely complex behavior and keeps on changing all the time, the energy efficiency is the most important key-point of routing algorithm. Many empirical measurements are inadequate to represent dynamic networks. However, the Quotient Space Theory is an in-depth treatment of hierarchical problem solving, and powerful abilities of representation with different granularities. In this paper, we present a novel approach based on Quotient Space Theory to reduce the computation complexity of routing algorithm in the dynamic network. Firstly, we analyze the structure of dynamic network and use community-based multi-granular representation to represent the network. Then we develop a routing algorithm based on multi-granular Spaces. Finally, we compare the proposed algorithm with several alternative methods and the results show that our algorithm clearly outperforms the comparison methods in the road network
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network performance analysis based on Quotient Space Theory
Granular Computing, 2013Co-Authors: Ling Zhang, Shu Zhao, Yuanting Yan, Yan Ping ZhangAbstract:Some performance analyses in complex network e.g., shortest path, etc. are complicated. Generally, human have natural ability to solve complex problems by approximating the optimal solution step by step. The granular computing model based on QST Quotient Space Theory provides not only a hierarchical description from fine to coarse but also an effective approach from coarse to fine to solve these complex problems. This paper proposes some methods on complex network performance analysis based on QST. Firstly, maximum cover network chain is used to solve the shortest path problem. Then, a method to find the optimal path of a weighted network is put forward. Finally, dynamic network is decomposed into a series of static networks to solve the maximum flow problem in dynamic network. Theoretical proofs and experimental results show that QST is an effective tool for complex problem solving.
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RSFDGrC - Network Performance Analysis Based on Quotient Space Theory
Lecture Notes in Computer Science, 2013Co-Authors: Ling Zhang, Shu Zhao, Yuanting Yan, Yan Ping ZhangAbstract:Some performance analyses in complex network e.g., shortest path, etc. are complicated. Generally, human have natural ability to solve complex problems by approximating the optimal solution step by step. The granular computing model based on QST Quotient Space Theory provides not only a hierarchical description from fine to coarse but also an effective approach from coarse to fine to solve these complex problems. This paper proposes some methods on complex network performance analysis based on QST. Firstly, maximum cover network chain is used to solve the shortest path problem. Then, a method to find the optimal path of a weighted network is put forward. Finally, dynamic network is decomposed into a series of static networks to solve the maximum flow problem in dynamic network. Theoretical proofs and experimental results show that QST is an effective tool for complex problem solving.
Liu Ren - One of the best experts on this subject based on the ideXlab platform.
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Texture Image Segmentation Based on Quotient Space
Computer Engineering, 2004Co-Authors: Liu RenAbstract:Theory of Quotient Space is a potential direction about research field based on artificial intelligence in recent years. The Theory of Quotient Space is applied to the analysis of texture image, and the texture feature is extracted from 8 neighborhood pixel cycle form, which would segment the texture image by analyzing the field features of texture image, and the test result of structural multi texture image is satisfactory. In the end, the paper concludes the process, during which the Quotient Space Theory is applied to work out the complicated questions, and this paper also demonstrates the practical value of the Theory.
Shu Zhao - One of the best experts on this subject based on the ideXlab platform.
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relational granulation method based on Quotient Space Theory for maximum flow problem
Information Sciences, 2020Co-Authors: Shu Zhao, Yan Ping Zhang, Jie Chen, Xian Sun, Zhen Duan, Yiwen ZhangAbstract:Abstract Granular computing (GrC) is a problem-solving concept deeply rooted in human thinking. GrC, as a new and rapidly growing paradigm of information processing, has attracted the attention of many researchers and practitioners. GrC is related to granulation, i.e., a process of drawing a set of objects (or points) together based on their indiscernibility, similarity, proximity, or functionality. In general, two types of granulation processes exist: functional granulation and relational granulation. If the process is based entirely on the attributes of the objects, it is known as functional granulation, whereas if the granulation process is based on the relationship between objects, it is known as relational granulation. This paper proposes a novel method, called the maximum flow based on Quotient Space Theory ( M F − Q S T ), for solving the maximum flow problems based on Quotient Space Theory for relational granulation. Using the method M F − Q S T , substructures are first detected, and the community is described as a substructure. Next, the relational granulation criterion is discussed in detail. The substructure that satisfies the relational granulation criterion is regarded as a coarse-grained node. Subsequently, the construction of a Quotient network that is coarser than the original network is described. Finally, the maximum flow algorithm is used to compute the maximum flow on the Quotient network as the approximated maximum flow on the original network within a much shorter period of time. Experimental results demonstrate that the novel method M F − Q S T reduces the cumulative running time after simplifying the network structure with a low error rate. The size of the Quotient network is significantly reduced, and the node and edge scales are reduced to 20.59% and 21.62% on average, respectively.
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A multi-granular network representation learning method
Granular Computing, 2019Co-Authors: Jie Chen, Shu Zhao, Xian Sun, Du Ziwei, Yan Ping ZhangAbstract:Granular computing (GrC) as a problem-solving concept and new information processing paradigm is deeply rooted in human thinking, which has attracted many researchers to study it theoretically, and has gradually applied to data-driven problems. Network embedding, as known as network representation learning, aiming to map nodes in network into a low-dimensional representation, is a data-driven problem. Most existing methods are based on a single granular, which learn representations from local structure of nodes. But global structure is important information on the network and has been proven to facilitate several network analysis tasks. Therefore, how to introduce GrC into network embedding to obtain a multi-granular network representation that preserves the global and local structure of nodes is a meaningful and tough task. In this paper, we introduce Quotient Space Theory, one of the GrC theories into network embedding and propose a Multi-Granular Network Representation Learning method based on Quotient Space Theory (MG_NRL, for short), which can preserve global and local structure at different granularities. Firstly, we granulate the network repeatedly to obtain a multi-granular network. Secondly, the embedding of the coarsest network is computed using any existing embedding method. Finally, the network representation of each granular layer is learned by recursively refining method from the coarsest network to original network. Experimental results on multi-label classification task demonstrate that MG_NRL significantly outperforms other state-of-the-art methods.
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network performance analysis based on Quotient Space Theory
Granular Computing, 2013Co-Authors: Ling Zhang, Shu Zhao, Yuanting Yan, Yan Ping ZhangAbstract:Some performance analyses in complex network e.g., shortest path, etc. are complicated. Generally, human have natural ability to solve complex problems by approximating the optimal solution step by step. The granular computing model based on QST Quotient Space Theory provides not only a hierarchical description from fine to coarse but also an effective approach from coarse to fine to solve these complex problems. This paper proposes some methods on complex network performance analysis based on QST. Firstly, maximum cover network chain is used to solve the shortest path problem. Then, a method to find the optimal path of a weighted network is put forward. Finally, dynamic network is decomposed into a series of static networks to solve the maximum flow problem in dynamic network. Theoretical proofs and experimental results show that QST is an effective tool for complex problem solving.
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RSFDGrC - Network Performance Analysis Based on Quotient Space Theory
Lecture Notes in Computer Science, 2013Co-Authors: Ling Zhang, Shu Zhao, Yuanting Yan, Yan Ping ZhangAbstract:Some performance analyses in complex network e.g., shortest path, etc. are complicated. Generally, human have natural ability to solve complex problems by approximating the optimal solution step by step. The granular computing model based on QST Quotient Space Theory provides not only a hierarchical description from fine to coarse but also an effective approach from coarse to fine to solve these complex problems. This paper proposes some methods on complex network performance analysis based on QST. Firstly, maximum cover network chain is used to solve the shortest path problem. Then, a method to find the optimal path of a weighted network is put forward. Finally, dynamic network is decomposed into a series of static networks to solve the maximum flow problem in dynamic network. Theoretical proofs and experimental results show that QST is an effective tool for complex problem solving.
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computing the point to point shortest path Quotient Space Theory s application in complex network
Rough Sets and Knowledge Technology, 2010Co-Authors: Yan Ping Zhang, Shu Zhao, Ling ZhangAbstract:The Quotient Space Theory can represent the world at different granularity sizes and deal with complicated problems hierarchically. We present significant improvement to point-to-point shortest path based on Quotient Space Theory in complex large-scale network. We propose the shortest path algorithm that is a heuristic method, in which evaluation function is based on community and hierarchical granularity decomposition of Quotient Space Theory. In preprocessing, we decompose large-scale network into some communities using hierarchical granularity decomposition of Quotient Space Theory, compute and store the minimum spanning trees in the communities and the shortest distance among communities. The implementation works on the large-scale road network. From experimental results, we know the proposed algorithm is effective and efficient in the road network of US.
Bo Zhang - One of the best experts on this subject based on the ideXlab platform.
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Quotient Space based problem solving a theoretical foundation of granular computing
2014Co-Authors: Ling Zhang, Bo ZhangAbstract:Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search. Explains the Theory of hierarchical problem solving, its computational complexity, and discusses the principle and applications of multi-granular computing Describes a human-like, theoretical framework using Quotient Space Theory, that will be of interest to researchers in artificial intelligence. Provides many applications and examples in the engineering and computer science area. Includes complete coverage of planning, heuristic search and coverage of strictly mathematical models.
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The Expansion of Quotient Space Theory
Quotient Space Based Problem Solving, 2014Co-Authors: Ling Zhang, Bo ZhangAbstract:In this chapter, we extend the Quotient Space Theory to general cases. It includes two aspects. First, the falsity- and truth-preserving principles are extended to a general Quotient Space approximation principle. Second, the Quotient Space Theory based on equivalence relations is extended to that based on tolerant relations and closure operations.
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Automatic Spatial Planning
Quotient Space Based Problem Solving, 2014Co-Authors: Ling Zhang, Bo ZhangAbstract:Automatic spatial planning, i.e. automatic robot planning, is discussed as one of the applications of Quotient Space Theory. We pay attention to how the Theory is applied to the problem, and how multi-granular computing can reduce its computational complexity.
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Fuzzy tolerance Quotient Spaces and fuzzy subsets
Science China Information Sciences, 2010Co-Authors: Ling Zhang, Bo ZhangAbstract:The structure and characteristic of fuzzy subsets are discussed by using the concepts of granulation and hierarchy in Quotient Space Theory. First, the equivalence relation based Quotient Space Theory is extended to the fuzzy tolerance relation. Second, the isomorphism and its discriminant of fuzzy tolerance relations are discussed. Finally, by using the fuzzy tolerance relation to define the fuzzy subset, its properties are addressed. The main results are given below: (1) several equivalent statements of fuzzy tolerance relations; (2) the definition of isomorphism of fuzzy tolerance relations; (3) the isomorphic discriminant of fuzzy tolerance relations; (4) the definition and properties of fuzzy subsets based on the fuzzy tolerance relations; and (5) the necessary and sufficient condition of the isomorphism of fuzzy subsets. These results will help us further comprehend the concepts of fuzzy tolerance relations and fuzzy subsets.
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GrC - Granular computing based on fuzzy and tolerance relations
2008 IEEE International Conference on Granular Computing, 2008Co-Authors: Bo Zhang, Ling ZhangAbstract:Granular computing (GC) as a new field has grown rapidly since the term was proposed. There are two basic concepts underlying GC, i.e., fuzziness/fuzzification and granularity/granulation. Fuzzy set Theory is an effective tool to deal with the fuzzification problem. There has been a large number of works on this aspect such as computing with words. The difficulty is in the concept of granulation. There was only an informal definition proposed by Zadeh for the concept. So far only a few works addressed the problem such as rough set and Quotient Space theories. In these works, the granulation is mainly defined by equivalence relations, i.e., a partition model. For example, in Quotient Space Theory, a problem is represented by a triplet (X,F,T), where X -the universe with the finest grain-size, F -the attribute of X, and T- the structure of X. When we view the same problem at a coarser grain size, we have a coarse-grained universe denoted by [X]. Then we have a new representation ([X],[F],[T]) of the problem. The coarse universe [X] is defined by an equivalence relation R on X. Then, representation ([X],[F],[T]) is called a Quotient Space of(X,F,T), where [X] -the Quotient set of X, [F] -the Quotient attribute of F, and [T] -the Quotient structure of T. Obviously, the set of representations of a problem at different granularities composes a complete semi-order lattice. But in many real applications, we must deal with non-partition models such as tolerance relations (or similarity relations, neighboring relations). In the talk, we will discuss the granular computing based on fuzzy and tolerance relations from the Quotient Space Theory point of view. We focus on the connections among different grain-size worlds, especially, the two basic properties between Quotient Spaces, i.e., falsity and truth preserving properties. We show that these two basic properties still hold in fuzzy and tolerance worlds. Using these properties, the computational complexity can be reduced either in problem solving or machine learning. We will extend the Quotient Space Theory based multi-granular computing from crispy world to fuzzy and tolerance worlds. The optimal path finding in complex networks is given as an example to show the application of the theoretical results.
