Deep machine learning for structural health monitoring on ship hulls using acoustic emission method
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Vassilios Kappatos | George Georgoulas | Petros Karvelis | Chrysostomos D. Stylios | C. Stylios | G. Georgoulas | V. Kappatos | P. Karvelis
[1] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[2] N. Ahmed,et al. Discrete Cosine Transform , 1996 .
[3] K.H.W. Seah,et al. The correlation of acoustic emission with rate of corrosion , 1993 .
[4] Eamonn J. Keogh,et al. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.
[5] Gilbert Strang,et al. The Discrete Cosine Transform , 1999, SIAM Rev..
[6] T. M. Roberts,et al. Acoustic emission monitoring of fatigue crack propagation , 2003 .
[7] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[8] Evangelos Dermatas,et al. Neural localization of acoustic emission sources in ship hulls , 2009 .
[9] Asa Prateepasen,et al. Study on source location using an acoustic emission system for various corrosion types , 2007 .
[10] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[11] SchmidhuberJürgen. Deep learning in neural networks , 2015 .
[12] T. W. Rettig,et al. Acoustic Emission Method for Monitoring Corrosion Reactions , 1976 .
[13] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[14] Chrysostomos D. Stylios,et al. A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors , 2015, IEEE Transactions on Industrial Informatics.
[15] Pingfeng Wang,et al. Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..
[16] Ge Wang,et al. Acoustic emission monitoring for detecting structural defects in vessels and offshore structures , 2009 .
[17] T. Berge,et al. ACOUSTIC EMISSION MONITORING OF CORROSION FATIGUE CRACK GROWTH IN OFFSHORE STEEL , 1984 .
[18] Andrew D. Ball,et al. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..
[19] Xue-wen Chen,et al. Combating the Small Sample Class Imbalance Problem Using Feature Selection , 2010, IEEE Transactions on Knowledge and Data Engineering.