A new and feasible trusted remediation model was built with its function and work flow explained in detail. Moreover, the communication and authentication process of remediation model were discussed. Simulation result...
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A new and feasible trusted remediation model was built with its function and work flow explained in detail. Moreover, the communication and authentication process of remediation model were discussed. Simulation results show that the proposed model can not only ensure the safety and reliability of network, but also provide remediation services for those terminal users who fail to meet the security policy demand.
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp...
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A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
Model-based diagnosis of discrete event systems is more and more active in artificial intelligence. In this paper, diagnosability analysis of discrete event systems is concerned, which is a very important step before ...
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Model-based diagnosis of discrete event systems is more and more active in artificial intelligence. In this paper, diagnosability analysis of discrete event systems is concerned, which is a very important step before on line diagnosing discrete event systems in general. Firstly, an extended hierarchical framework for definitions of diagnosability of discrete event systems is given, according to their inner restriction. Next, some formal comparisons among them are presented, thanks to which, we can further understand the relations between related definitions. Finally, some future work about diagnosability of discrete event systems is discussed as well.
WiMax (World Interoperability for Microwave Access) technology is the hotspot of wireless access technologies and attracts much attention. WiMax technology provides a wireless access method for users and do not constr...
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WiMax (World Interoperability for Microwave Access) technology is the hotspot of wireless access technologies and attracts much attention. WiMax technology provides a wireless access method for users and do not constrained by physical position and cable restriction. A communication platform was designed under the protocol WiMax. The platform contains client (Vehicles) and server (Base stations). The platform contains 5 function modules including stimulating moving, position information transfer, sound communication, file transfer and routing selection. Building a mature communication platform is the most important precondition to improve the transfer efficiency in intelligent transport system. Intelligent transport system with the characteristic of low cost, less budget and high speed of transferring information is urgently needed by every city.
Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between variables. But Learning BNs is ...
Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between variables. But Learning BNs is a NP hard problem. This paper presents an immune genetic algorithm for learning Markov equivalence classes, which combining dependency analysis and search-scoring approach together. Experiments show that the immune operators can constrain the search space and improve the computational performance.
Hierarchy is a remedy way to reduce the demanding complexity of model-based diagnosis. In this paper, an approach to diagnosis of discrete-event systems in a hierarchical way is proposed, inspired by the concept "...
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Hierarchy is a remedy way to reduce the demanding complexity of model-based diagnosis. In this paper, an approach to diagnosis of discrete-event systems in a hierarchical way is proposed, inspired by the concept "D-holon" and the concept "Silent Closure" presented in the literatures recently. Each extended silent closure can be seen as a special type of D-holons, called SCL-D-holon. Every hierarchical level is an SCL-D-holon built off line. When on line diagnosing a discrete-event system, only related SCL-D-holons will be called instead of all the SCL-D-holons generally, thus the space complexity is reduced. In comparison to on line creating silent closures, the efficiency is improved as well.
This paper first gives an analysis of data aggregation and data compression based on energy consumption of sensor nodes, after which an approach is proposed to construct an aggregation tree in the case of non-perfect ...
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This paper first gives an analysis of data aggregation and data compression based on energy consumption of sensor nodes, after which an approach is proposed to construct an aggregation tree in the case of non-perfect aggregation, since GIT considers only the case of perfect aggregation and it does not work well if the aggregation is non-perfect. An assessment scheme that can get the information of hops from the aggregation point to the sink and the hops from the aggregation point to the source node is used to construct such an aggregation tree. Moreover, the energy consumption of the aggregation is also considered. This scheme can be used when perfect aggregation cannot be performed. In this paper, an approach to reduce the cost of reinforcement is also proposed, in which the reinforcement work is done by the source nodes themselves, not by the sink node. Simulation result shows that this approach can save more energy than GIT when the aggregation ratio is small. This result also provides a theoretical limit of aggregation to tell when GIT will lose its superiority and thus gives a direction to choose among the aggregation algorithms. Another result shows that the further the sources are away from the sink, the less reinforcement messages are needed. Finally a guidance to tell when to use the EGA (energy consumption assessment) scheme is given.
Proper ontology definition is the prerequisite for efficient knowledge acquisition. For the complex knowledge that could not be described by simple binary relation, we advocated a methodology for aggregated knowledge ...
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ISBN:
(纸本)9781424430536;9780769531519
Proper ontology definition is the prerequisite for efficient knowledge acquisition. For the complex knowledge that could not be described by simple binary relation, we advocated a methodology for aggregated knowledge acquisition, describing how to define the ontology for such aggregated knowledge concept and how to acquire knowledge basing on such definition. Experiment shows that this methodology is effective in automatic knowledge acquisition from Chinese free text.
DNA-binding proteins play an important role invarious intra-and extra-cellular *** key in theprotein is DNA-binding region also called DNA-bindingdomain(DBD).However,it is hard to search the DBDsby means of homology s...
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DNA-binding proteins play an important role invarious intra-and extra-cellular *** key in theprotein is DNA-binding region also called DNA-bindingdomain(DBD).However,it is hard to search the DBDsby means of homology search or hidden Markov modelsbecause of a wide variety of the *** this work,we develop a kernel-based machine learning method bycombination of multiple "l-vs-l" binary classifiers forDNA binding domain *** result shows that93.73％ accuracy is achieved for multicategory classifierand no less than 90％ accuracy for each binary *** comparison,our classifier performs better than othermachine learning methods.
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