Abstract
A wavelet morphometric neural network algorithm for analyzing the porous texture of a nanomaterial that differs from the use of the procedure for identifying the binary clusters for the morphometric analysis obtained as a result of the analysis of the neural network cluster of a micro photo image of a nanomaterial instead of the procedure for identifying binary objects on binary cross sections of the original micro photo image have been proposed. Using this algorithm, we calculated the quantitative morphometric estimations of the geometric parameters of the nanocluster texture of solid and porous components, which have been applied to predict the density distribution of pores inside of a nanomaterial.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Sarkisov, P.D., Butusov, O.B., and Meshalkin, V.P., Wavelet Decomposition-Based Morphometric Algorithm for Analyzing Photomicrographs of Texture of Solid-Phase Nanomaterials, Dokl. Chem., 2010, vol. 434, part 2, p. 269.
Sarkisov, P.D., Butusov, O.B., Meshalkin, V.P., et al., Computer-Aided Method of Analysis of Nanocomposite Structure on the Basis of Calculations of Isolines of Fractal Dimensionality, Theor. Found. Chem. Eng., 2010, vol. 44, no. 6, p. 838.
Sarkisov, P.D., Butusov, O.B., and Meshalkin, V.P., Computer-Aided Tools for Molecular Systems Engineering and Wavelet-Morphometric Analysis of the Texture of Nanomaterials, Theor. Found. Chem. Eng., 2011, vol. 45, no. 1, p. 1.
Hui, L., Smith, R.C., Wang, X., Nelson, J.K., and Schadler, L.S., Quantification of Particulate Mixing in Nanocomposites, in Electrical Insulation and Dielectric Phenomena, Quebec: Inst. of Electrical and Electronic Engineers, 2008, p. 317.
Lebedev, A. and Sbruev, S., SiC Electronics: The Past, Present, and Future, Elektron.: Nauka, Tekhnol., Biznes, 2006, no. 6, p. 28.
Filonov, K.N., Kurlov, V.N., Klassen, N.V., et al., Peculiarities of Nanostructured Silicon Carbide Films and Coatings Obtained by Novel Technique, Bull. Russ. Acad. Sci.: Phys., 2009, vol. 73, no. 10, p. 1374.
Mirkin, B., Clustering for Data Mining: A Data Recovery Approach, London: Taylor and Francis, 2005.
Abonyi, J. and Feil, B., Cluster Analysis for Data Mining and System Identification, Basel: Birkhauser, 2007.
Serezhkin, V.N., Pushkin, D.V., Sevast’yanov, V.G., et al., A New Method for Analyzing Intermolecular Interactions in Crystal Structures: Metal Carbonyls, Russ. J. Inorg. Chem., 2005, vol. 50, no. 12, p. 1893.
Khaikin, S., Neironnye seti: Polnyi kurs (Neural Networks: A Complete Course), Moscow: Vil’yams, 2006.
Tarkov, M.S., Neirokomp’yuternye sistemy (Neurocomputer Systems), Moscow: INTUIT-BINOM, 2006.
Butusov, O.B. and Meshalkin, V.P., Computation of the Integral Parameters of Turbulent Structures for the Transient Gas Flows in Pipes Using Wavelet Transforms, Theor. Found. Chem. Eng., 2008, vol. 42, no. 2, p. 160.
Tret’yakov, Yu.D., Self-Organization Processes in the Chemistry of Materials, Russ. Chem. Rev., 2003, vol. 72, no. 8, p. 651.
Chou, C.H., Su, M.C., and Lai, E., A New Cluster Validity Measure for Clusters with Different Densities, IASTED Int. Conf. on Intelligent Systems and Control, Salzburg, 2003. p. 276.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © P.D. Sarkisov, O.B. Butusov, V.P. Meshalkin, V.G. Sevastianov, A.B. Galaev, E.G. Vinokurov, 2012, published in Teoreticheskie Osnovy Khimicheskoi Tekhnologii, 2012, Vol. 46, No. 4, pp. 386–395.
Rights and permissions
About this article
Cite this article
Sarkisov, P.D., Butusov, O.B., Meshalkin, V.P. et al. Wavelet morphometric neural network algorithm for analyzing nanomaterial porous texture. Theor Found Chem Eng 46, 329–337 (2012). https://doi.org/10.1134/S004057951204015X
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S004057951204015X