Kazushi Yamamoto
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Kazushi Yamamoto has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Daisuke Yamashita
Department of Physics, University of Toyama, Japan
1 shared publication - Hideaki Touyama
Department of Information Systems Engineering, Faculty of Information Engineering, Toyama Prefectural University, Imizu 939-0398, Japan
1 shared publication - Hiroshi Okuda
Department of Materials Science and Engineering, Kyoto University
1 shared publication - Hiroyuki Ikemoto
Department of Physics, University of Toyama, 3190 Gofuku, Toyama, 930-8555, JAPAN
1 shared publication - Masataka Nakamura
Department of Physics, University of Toyama, Japan
1 shared publication
Latest Publications
- 20 May 2020
- Published by International Union of Crystallography (IUCr) in Journal of Synchrotron Radiation
- Vol. 27 (4) , 1069-1073
Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network
Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations.
- 20 May 2020
- Published by International Union of Crystallography (IUCr) in Journal of Synchrotron Radiation
- Vol. 27 (4) , 1069-1073
Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network
Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations.