A lightning prediction model is established to predict the lightning in 24h in Chongqing, the model is based on the character of lightning weather, the advantages of the support vector machine (SVM) method in solving ...
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A lightning prediction model is established to predict the lightning in 24h in Chongqing, the model is based on the character of lightning weather, the advantages of the support vector machine (SVM) method in solving learning problems of nonlinear and high dimensional samples, and the class-weighted dual v-SVM (WDv-SVM) -an improved algorithm of SVM. According to high-altitude and surface data during 1998 to 2008 provided by the Micaps system in China Meteorological Administration and the lightning observation data collected from 35 ground stations all over the city, the predictors related to lightning occurred are calculated. Compared with c-SVM and v-SVM, WDv-SVM is provided with superior classification accuracies and prediction accuracies. Consequently, the lightning prediction system in operational application is developed on the basis of the model referred.
The variations in testing requirements often result in the problem of numerous test cases and low efficiency in regression testing. To solve this problem, a reduction method of testing requirements is proposed based o...
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The variations in testing requirements often result in the problem of numerous test cases and low efficiency in regression testing. To solve this problem, a reduction method of testing requirements is proposed based on the relation mode in this paper. This method analyzes the relationship between the modules and testing requirements, then determines the varied regression testing requirements, and gets the final testing requirement set by reduction of the varied regression testing requirement set and the original testing requirement set in three times. If test cases are generated and reduced on the basis of the testing requirement reduction set, the efficiency of the regression testing will be improved.
In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. ...
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In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. Computational tests show that this hybrid method is very effective and robust for the elimination ordering problem.
According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are prop...
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According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem.
To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heu...
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To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. Computational experiments show that MHC-HS-ACS can find very high quality solutions.
In wireless sensor networks, since the existing methods of dividing sensors based on the random deployment of nodes can not guarantee the optimal deployment to target coverage, a probabilistic disc model based optimal...
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In wireless sensor networks, since the existing methods of dividing sensors based on the random deployment of nodes can not guarantee the optimal deployment to target coverage, a probabilistic disc model based optimal deterministic deployment scheme of sensor nodes was proposed. Firstly, probabilistic disc model was used to capture the stochastic nature of sensing. And, node sensing radius meeting to user needs was computed. Secondly, the candidate positions where nodes were placed to coverage target set were computed by using the concept of the most multi-overlapping domains of target point. Finally, known the candidate positions, by using simulated annealing genetic algorithm, the optimal positions and the least number of nodes to coverage target set were gained. Simulation results show that the nodes optimal deployment method is obtained based on user needs. The optimal allocation of resources is realized in wireless sensor networks.
With the wide use of online social networks (OSNs), the problem of data privacy has attracted much attention. Several approaches have been proposed to address this issue. One of privacy management approaches for OSN l...
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With the wide use of online social networks (OSNs), the problem of data privacy has attracted much attention. Several approaches have been proposed to address this issue. One of privacy management approaches for OSN leverages a key management technique to enable a user to simply post encrypted contents so that only users who can satisfy the associate security policy can derive the key to access the data. However, the key management policies of existing schemes may grant access to unauthorized users and cannot efficiently determine authorized users. In this paper, we propose a collaborative framework which enforces access control for OSN through an innovative key management focused on communities. This framework introduces a community key management based on a new group-oriented convergence cryptosystem, as well as provides an efficient privacy preservation needed in a private OSN. To prove the feasibility of our approach, we also discuss a proof-of-concept implementation of our framework. Experimental results show that our construction can achieve the identified design goals for OSNs with the acceptable performance.
This paper presents a discriminative model for part of speech tagging of traditional *** use Maximum Entropy Model with Morphological features of Mongolian. First, the context feature templates are defined and extract...
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This paper presents a discriminative model for part of speech tagging of traditional *** use Maximum Entropy Model with Morphological features of Mongolian. First, the context feature templates are defined and extracted from the training corpus. Then, the parameters of maximum entropy probability models are calculated. Experimental results show that integration of morphological features of Maximum Entropy Model for Mongolian part of speech tagging outperform HMM since they are flexible enough to capture many correlated non-independent features.
A multiparty simultaneous quantum identity authentication protocol based on Creenberger-Horne-Zeilinger （GHZ） states is proposed. The multi-user can be authenticated by a trusted third party （TTP） simultaneously. ...
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A multiparty simultaneous quantum identity authentication protocol based on Creenberger-Horne-Zeilinger （GHZ） states is proposed. The multi-user can be authenticated by a trusted third party （TTP） simultaneously. Compared with the scheme proposed recently （Wang et al 2006 Chin. Phys. Lett. 23（9） 2360）, the proposed scheme has the advantages of consuming fewer quantum and classical resources and lessening the difficulty and intensity of necessary operations.
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