Defect characteristic prediction of pipeline by means of wavelet neural network based on the hierarchical clustering algorithm

Proceedings of the Biennial International Pipeline Conference, IPC

Volumn 2, Issue , 2004, Pages 921-924

  Wei, Maoan ^a   Jin, Shijiu ^a   Wang, Likun ^a   Zhou, Van ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Hierarchical clustering algorithm; ISODATA algorithm; K means algorithm; Wavelet basis function neural network

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DEFECTS; FINITE ELEMENT METHOD; LEAKAGE (FLUID); NEURAL NETWORKS;

HIERARCHICAL CLUSTERING ALGORITHMS; ISODATA ALGORITHMS; K-MEANS ALGORITHMS; WAVELET BASIS FUNCTION NEURAL NETWORKS;

PIPELINES;

EID: 18144374177     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/ipc2004-0722     Document Type: Conference Paper

References (5)

1. Hwang, K., 2000, "3-D defect profile reconstruction from magnetic flux leakage signatures using wavelet basis function neural networks", Ph.D. Dissertation, Iowa State University, Ames, IA.
2. Ivanov, P.A., Zhang, Z., Yeoh C.H., et al., 1998, "Magnetic Flux Leakage Modeling for Mechanical Damage in Transmission Pipelines", IEEE Transactions on Magnetics, 34 (5), pp: 50-65.
3. Wei, M., Jin, S., Li, Y.,2003,2D Profile Reconstruction and Process Technology for Pipeline, Acta Petrolei Sinica, 24(6), pp: 98-101
4. Wu Q., Shen X., Yang Q., et al., 1997 "Using Wavelet Neural Networks for the Optimal Design of Electromagnetic Devices", IEEE Transactions on Magnetics, 33(2), pp: 120-132
5. Tou, J., and Gonzalez, R., 1974, "Pattern recognition principles", Addison-Wesley Publishing Company, pp:300-320