MATEC Web Conf.
Volume 103, 2017International Symposium on Civil and Environmental Engineering 2016 (ISCEE 2016)
|Number of page(s)||6|
|Section||Structure, Solid Mechanics and Computational Engineering|
|Published online||05 April 2017|
A Preliminary Study Application Clustering System in Acoustic Emission Monitoring
Jamilus Research Center, Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor
* Corresponding author: email@example.com
Acoustic Emission (AE) is a non-destructive testing known as assessment on damage detection in structural engineering. It also can be used to discriminate the different types of damage occurring in a composite materials. The main problem associated with the data analysis is the discrimination between the different AE sources and analysis of the AE signal in order to identify the most critical damage mechanism. Clustering analysis is a technique in which the set of object are assigned to a group called cluster. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of data. In this paper was used k-means algorithm for partitioned clustering method, numerous effort have been made to improve the performance of application k-means clustering algorithm. This paper presents a current review on application clustering system in Acoustic Emission.
© 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.
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