Mobile node localization is one of the challenging and crucial issues in wireless sensor networks. The paper proposed a new approach to mobile localization, called LLA (Lee Localization Algorithm), to mitigate TOA mea...
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In order to solve the security issues of mobile vehicles in the Internet of vehicles, such as identity authentication and integrality verification, we apply the trusted cryptography module (TCM) into the on-vehicle el...
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In order to solve the security issues of mobile vehicles in the Internet of vehicles, such as identity authentication and integrality verification, we apply the trusted cryptography module (TCM) into the on-vehicle electronic systems. TCM has many security features, such as integrality measurement, integrality report and protected storage, by which the on-vehicle electronic systems achieve a trusted boot process. Data and information of vehicles are encrypted before storage and transmission. The experiments show that with the authorization on the vehicular platform, the TCM will improve the credibility and security of the Internet of Vehicles.
The rigorous theoretical analyses of algorithms for #SAT have been proposed in the literature. As we know, previous algorithms for solving #SAT have been analyzed only regarding the number of variables as the paramete...
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For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population divers...
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ISBN:
(纸本)9788988678251
For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA posses better performance and robustness than other swarm intelligence methods.
Petri Nets is a powerful mathematical modeling tool for system description and analysis, with which we can describe the relationship among entities efficiently. The present thesis puts forward the modeling of the dist...
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We researched one search algorithm based on direction in the unstructured P2P network, analyzed the limited insufficiencies of this algorithm to the search speed, we proposed the improved direction search algorithm ba...
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We have studied the AC-4 algorithm and then present key value ordering heuristic forming the new solving algorithm BT-KVV, which is based on the AC-4 algorithm. This algorithm takes full advantage of the state inf...
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ISBN:
(纸本)9781424479573
We have studied the AC-4 algorithm and then present key value ordering heuristic forming the new solving algorithm BT-KVV, which is based on the AC-4 algorithm. This algorithm takes full advantage of the state information of the data structure used in the AC-4 algorithm after the process of arc consistency. The algorithm sorts the values of the variables' domain according to the key importance of the values. So this order forces the solving algorithm to give priority to extend the key values of variables. In this way, the efficiency of the solving algorithm can be improved a lot. The result of our experiments shows that our algorithm has much more advantage over other solving algorithms.
Automatic image annotation is an active topic and difficult task in computer vision domain, which has attracted more and more researchers' attention. Many approaches have been proposed to automatically annotate im...
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Vehicular ad hoc network (VANET) is a kind of self-organizing ad hoc network, which is specifically designed for communication among vehicles. In VANET, a source vehicle must rely on intermediate vehicles to forward i...
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Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number ...
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Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number of illegal structures is exponential, which infect the efficiency of structure learning;(2) comparing the structures in same equivalent class slow down the speed of convergence;(3) if the prior distribution for each structure is equal, the more structures contain in the equivalent class the higher prior probability of the class has. This paper employs two competitive bio-inspired algorithms, immune algorithm and co-evolutionary algorithm, for learning Essential graph. The algorithm combines dependency analysis and search-scoring approach together. Experiments show that the searching space was decreased, compare with prior works, the convergence speed and the efficiency was improved.
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