This paper addresses the problem of failure diagnosis in component-based discrete event systems. In this paper we propose a method to obtain the set of components when dealing with diagnosis in large complex discrete ...
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This paper addresses the problem of failure diagnosis in component-based discrete event systems. In this paper we propose a method to obtain the set of components when dealing with diagnosis in large complex discrete event systems. In the new method, before disassembling the system into components, we need to identify whether insert communication events into the system or not. When analyzing the diagnosability, we treat the system containing communication events as a distributed discrete event system. Otherwise we treat the system as a decentralized discrete event system. For the components which are not diagnosable, we propose a method to reconstruct them by utilizing some other components sharing the same communication events with them. This algorithm provides more accurate information of the diagnosability of the system.
A novel self-adaptive differential evolution (SADE) algorithm is proposed in this paper. SADE adjusts the mutation rate F and the crossover rate CR adaptively, taking account of the different distribution of populatio...
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Community structure is one of non-trivial topological properties ubiquitously demonstrated in real-world complex networks. Related theories and approaches are of fundamental importance for understanding the functions ...
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Escape time algorithm is a universal algorithm when to create fractal image. A class of algorithms based on escape time algorithm is wasting-calculation. In this essay, when combined with the feature of eventually per...
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Collective classification in networked data has become an important and active research topic, it has a wide variety of real world applications, such as hyperlinked document classification, protein interaction and gen...
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In this paper, we introduce a new tractable subclass of cardinal direction relations we called strong saturated-convex rectangle cardinal direction relations. We prove that reasoning in this subclass is a polynomial t...
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In this paper, we introduce a new tractable subclass of cardinal direction relations we called strong saturated-convex rectangle cardinal direction relations. We prove that reasoning in this subclass is a polynomial time problem and show that the path-consistency method is sufficient for deciding consistency.
Recently evolutionary search has been investigated by lots of researchers as method for structure learning of Bayesian network. In order to avoid the problem of premature convergence and enhance the learning accuracy,...
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Recently evolutionary search has been investigated by lots of researchers as method for structure learning of Bayesian network. In order to avoid the problem of premature convergence and enhance the learning accuracy, in this paper we integrated evolutionary way of bees and proposed triple-population evolution strategies to learn Bayesian network structure. During evolution, the best Bayesian network individual is considered as queen bee, and there are three populations: one stores and evolves elitist individuals, another stores random individuals to guarantee the diversity of individuals, some individuals from that two populations form the third population in which every individual is considered as drone and mated with queen bee to generate offspring. The proposed method has both exploitative and explorative ability. Experimental results show that the proposed method can converge to more accurate Bayesian network than standard genetic algorithm based learning method.
Bayesian network is a popular tool for uncertainty process in Artificial Intelligence. In recent years, more and more attention has been paid to learning of Bayesian network. In this paper, we proposed a novel learnin...
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Bayesian network is a popular tool for uncertainty process in Artificial Intelligence. In recent years, more and more attention has been paid to learning of Bayesian network. In this paper, we proposed a novel learning algorithm for Bayesian network based on (μ, λ)-Evolution Strategy, we present the encoding scheme and fitness function, designed the evolutionary operators of recombination, mutation and selection. Theoretical analysis and experimental results all demonstrate that the proposed method can learn the Bayesian network from data effectively.
The logical difference is important to ontology engineers in capturing and understanding the difference between different versions of given ontology. For acyclic EL terminologies, in which the well applied medical ont...
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The logical difference is important to ontology engineers in capturing and understanding the difference between different versions of given ontology. For acyclic EL terminologies, in which the well applied medical ontology SNOMED CT is represented, there are two methods proposed in computing the logical difference between terminologies: direct computation method and uniform interpolant method. We argue that the later method outperforms the former one in showing the dependency between entailments in the logical difference through the introduction of concept difference. The resulting logical difference conveys more information to ontology engineers than direct computation method.
Community mining has been the focus of many recent researches on dynamic social networks. In this paper, we propose a clustering based improved ant colony algorithm (CIACA) for community mining in social networks. The...
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Community mining has been the focus of many recent researches on dynamic social networks. In this paper, we propose a clustering based improved ant colony algorithm (CIACA) for community mining in social networks. The CIACA combines the local pheromone update rule with the global update rule and utilizes heuristic function to adjust the clustering solution dynamically, assisted by decay coefficient of dynamic network model. In order to improve clustering accuracy and convergence rate in the process of ant migration, a structure tightness between nodes based clustering centers initializing method is proposed, which can provide us initial clustering centers with certain clustering precision and high diversity. In addition, random number and specific parameter are used in the ant transition probability, which strengthens the search stochastic properties of CIACA effectively. The proposed CIACA is tested on some benchmark social networks, and is compared with current representative algorithms in community mining. Experimental results show the feasibility and validity of CIACA.
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