A machine learning approach for asperities’ location identification-Reference-Cited by-同舟云学术

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A machine learning approach for asperities’ location identification

Published:2017-10-05 Issue:1 Volume:10 Page:41-50
ISSN:1868-6478
Container-title:Evolving Systems
language:en
Short-container-title:Evolving Systems

Author:

Arvanitakis Konstantinos,Karydis Ioannis,Kermanidis Katia L.,Avlonitis Markos

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s12530-017-9204-x.pdf

Reference38 articles.

1. Aki K (1965) Maximum likelihood estimate of b in the formula logn = a-bm and its confidence limits. Bull Earthq Res Inst 43:237–239

2. Arvanitakis K, Avlonitis M (2016) Identifying asperity patterns via machine learning algorithms. In: IFIP International conference on artificial intelligence applications and innovations. Springer, pp 87–93

3. Avlonitis M, Kalaitzidou K (2015) Estimating the real contact area between sliding surfaces by means of a modified OFC model. Arch Civ Mech Eng 15(2):355–360

4. Avlonitis M, Papadopoulos GA (2014) Foreshocks and b value: bridging macroscopic observations to source mechanical considerations. Pure Appl Geophys 171(10):2569–2580

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

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4. Introducing stochastic recurrence interval to classification algorithms for identifying asperity patterns;Physica A: Statistical Mechanics and its Applications;2018-12

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