A hybrid of graph-based and neural network recognition system is developed. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are ...
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A hybrid of graph-based and neural network recognition system is developed. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are first checked to see whether they match with the predefined feature library. If so, a feature vector is assigned to them. Otherwise, base faces are obtained as heuristic information and used to restore the missing faces and update the sub-graphs. The sub-graphs are transformed into vectors, and these vectors are presented to the neural network, which classifies them into feature classes. The scope of instances variations of predefined feature that can be recognized is very wide. A new BP algorithm based on the enlarging error is also presented.
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.
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.
We present a novel object localization approach based on the global structure constraint model (GSC) and optimal algorithm. In GSC, Objects are described as constellations of points satisfied with their specific globa...
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ISBN:
(纸本)9781424431977;9780769531618
We present a novel object localization approach based on the global structure constraint model (GSC) and optimal algorithm. In GSC, Objects are described as constellations of points satisfied with their specific global structure constraints. The spatial relations among all the patches having stable color information and their representative color information around patches are encoded. Then, the searching algorithm, i.e. genetic algorithm, is used to locate target objects in images by finding out the exactly matched position. In the experiment, we tested the approach on a collection of human face images and the results demonstrated the approach is simple, effective and efficient.
Attribute discretization is one of the key issues for the Rough Set theory. First, a method is proposed to compute an initial cut points set. The indistinguishable relation of decision tables did not change, and the n...
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Attribute discretization is one of the key issues for the Rough Set theory. First, a method is proposed to compute an initial cut points set. The indistinguishable relation of decision tables did not change, and the number of elements in the initial cut points set was reduced. Then, the cut point information entropy was defined to measure the importance of a cut point. Finally, an attribute discretization algorithm based on the Rough Set and information entropy was proposed. The consistence of decision tables did not change, and the mixed decision table was considered, which contains continuous and discrete attributes. The experimental results show that this algorithm is effective and is competent for processing the large-scale datasets.
An efficient methodology for recognizing features is presented. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are first checke...
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An efficient methodology for recognizing features is presented. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are first checked to see whether they match with the predefined feature library. If so, a feature vector is assigned to them. Otherwise, base faces are obtained as heuristic information and used to restore missing faces, meanwhile, update the sub-graphs. The sub-graphs are transformed into vectors, and these vectors are presented to the neural network which classifies them into feature classes.
Aiming at the dynamic policy trigger problem in policy based network management, this thesis first introduces the uncertain information generated by one-point coverage random sets theory and also the unified framework...
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Aiming at the dynamic policy trigger problem in policy based network management, this thesis first introduces the uncertain information generated by one-point coverage random sets theory and also the unified framework of fuzzy information. On the basis of which, according to the feature of policy based network management, a model used to update the predicted information using present known information, experience, and knowledge is designed. At the same time, this thesis put forward that, take the semblance of policy as constant probability in product space conditional event algebra, so as to execute information fusion of multiple sets of related policy. In the end, compared with the policy based inference system and then analyze the result.
A framework of hiberarchy trusted network basedon the grade division was put forward,and thepartition rules of trusted attributes as well as themethods of grade division were explained in ***,the potential application...
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A framework of hiberarchy trusted network basedon the grade division was put forward,and thepartition rules of trusted attributes as well as themethods of grade division were explained in ***,the potential applications of this frameworkin trusted network were discussed,and the accessprocedure of terminals in hiberarchy trusted networkwas given based on the existing research *** show that by the proposed framework,notonly the safety and reliability of network can beensured,but also the flexibility of ways to accessnetwork is strengthened,and that the framework canprovide supports for interoperability of differentequipment suppliers.
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