MATEC Web Conf.
Volume 139, 20172017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|Number of page(s)||4|
|Published online||05 December 2017|
Rock Granularity Analysis by Deep Belief Network
1 School of Computer Sciences, Computer Department, 71006 Xi’an Shiyou University. China
2 School of Computer Sciences, Computer Department, 71006 Xi’an Shiyou University. China
Granularity analysis is one of the most essential issues in authenticate. To improve the identification accuracy, a Deep Belief Network (DBN) based method is proposed in this paper. DBN can extract features from image samples automatically.4800 rock images from Ordos Basin are used in this paper, 1200 of them are used to test the model accuracy and the result achieves 94.75%. According to theoretical and actual data test, the method proposed in this paper is efficiency and accuracy.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.