ICA Mixture Modeling for the Classification of Materials in Impact-Echo Testing

A process and an apparatus provide a purified material by employing a rate of condensation of the material which is substantially greater than the rate of solidification of the material. Telleurium and cadmium are effectively purified by the process.

[1]  Stephen J. Roberts,et al.  Variational Mixture of Bayesian Independent Component Analyzers , 2003, Neural Computation.

[2]  C. F. Morabito,et al.  Independent component analysis and feature extraction techniques for NDT data , 2000 .

[3]  Mary Sansalone,et al.  Impact-echo : nondestructive evaluation of concrete and masonry , 1997 .

[4]  Colorado Colorado,et al.  AMERICAN SOCIETY OF CIVIL ENGINEERS , 2010 .

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  J. Igual,et al.  DATA CLUSTERING METHODS BASED ON MIXTURE OF INDEPENDENT COMPONENT ANALYZERS , 2006 .

[7]  M. Lewicki,et al.  Learning higher-order structures in natural images , 2003, Network.

[8]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[9]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[10]  Allan Kardec Barros,et al.  Independent Component Analysis and Blind Source Separation , 2007, Signal Processing.

[11]  Ted von Hippel,et al.  Automated classification of stellar spectra - II. Two-dimensional classification with neural networks and principal components analysis , 1998, astro-ph/9803050.

[12]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[13]  Russell H. Lambert,et al.  OVERDETERMINED BLIND SOURCE SEPARATION: USING MORE SENSORS THAN SOURCE SIGNALS IN A NOISY MIXTURE , 2000 .

[14]  Nicholas J. Carino,et al.  Transient stress waves interaction with planar flaws , 1988 .

[15]  David G. Stork,et al.  Pattern Classification , 1973 .

[16]  Terrence J. Sejnowski,et al.  ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  R. Xu,et al.  Application of principal component analysis to the FTIR spectra of disk lubricant to study lube-carbon interactions , 2004, IEEE Transactions on Magnetics.

[18]  Alberto Prieto,et al.  Computational and ambient intelligence , 2009, Neurocomputing.

[19]  俊一 甘利,et al.  A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, Jhon Wiley & Sons, 2001年,504ページ. (根本幾・川勝真喜訳:独立成分分析——信号解析の新しい世界,東京電機大学出版局,2005年,532ページ.) , 2010 .

[20]  Luis Vergara,et al.  A Blind Source Separation Technique for Extracting Sinusoidal Interferences in Ultrasonic Non-Destructive Testing , 2004, J. VLSI Signal Process..

[21]  Addisson Salazar,et al.  ICA Model Applied to Multichannel Non-destructive Evaluation by Impact-Echo , 2004, ICA.

[22]  Addisson Salazar,et al.  Neural Networks for Defect Detection in Non-destructive Evaluation by Sonic Signals , 2007, IWANN.

[23]  E. Oja,et al.  Independent Component Analysis , 2013 .

[24]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[25]  J. Gosálbez,et al.  Blind source separation for classification and detection of flaws in impact-echo testing , 2005 .

[26]  Addisson Salazar,et al.  Two Applications of Independent Component Analysis for Non-destructive Evaluation by Ultrasounds , 2006, ICA.

[27]  Nicholas J. Carino,et al.  The Impact-Echo Method: An Overview , 2001 .

[28]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .