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Your search keyword '"Wei, Rong"' showing total 24 results
Start Over Author "Wei, Rong" Publication Type Books
24 results on '"Wei, Rong"'

19. Antioxidants and Cancer Prevention

Author

Kornhauser, Andrija, primary, Lambert, Lark A., additional, Wamer, Wayne G., additional, Wei, Rong Rong, additional, Lavu, Sukadev, additional, and Timmer, William C., additional

Published
1995
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23. An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Simultaneous Pickup and Delivery.

Author

Wei, Rong, Zhang, Tongliang, and Tang, Hui

Abstract

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a variant of the Capacitated Vehicle Routing Problem (CVRP), in which clients require both pickup and delivery services. This paper proposes an improved particle swarm optimization algorithm based on multiple social structures for solving VRPSPD. The decoding of single particle in swarm consists two parts: the first is m dimensional (m-D) variables for m customers, and the second comprises 2n dimensions (2n-D) for n vehicles which presents vehicle route orientation. The particle is transformed to customers΄ list and vehicles matrix. A benchmark dataset is used to validate the performance of proposed algorithm. Comparing with prior works, promising results indicate that the proposed algorithm may hold high potentials for generating powerful tool for solving VRPSPD and other attributes of vehicle routing problem. [ABSTRACT FROM AUTHOR]

Published
2011
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24. Using Fuzzy Support Vector Machine Network to Predict Low Homology Protein Structural Classes.

Author

Istrail, Sorin, Pevzner, Pavel, Waterman, Michael S., Rajapakse, Jagath C., Schmidt, Bertil, Volkert, Gwenn, Zhang, Tongliang, Wei, Rong, and Ding, Yongsheng

Abstract

Prediction of protein structural classes for low homology proteins is a challenging research task in bioinformatics. A dual-layer fuzzy support vector machine (FSVM) network approach is proposed to predict protein structural classes. A protein sample can be represented by nine representation feature vectors: pair couple amino acid (210-D) and eight pseudo amino acid composition vectoers (PseAAC). Eight physicochemical properties of amino acids extracted from AAIndex databank are used to calculate low frequencies of power spectrum density of sequence-order correlation in protein sequence. In the first layer of FSVM network, nine FSVM classifiers are established, which are trained by different protein feature vectors, respectively. The outputs of the first layer are reclassified by FSVM classifier in 2nd layer of the network. The performance of proposed method is validated by low homology (average 25%) dataset covering 1673 proteins. The promising results indicate that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes. [ABSTRACT FROM AUTHOR]

Published
2007
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