Unsupervised learning approach to automation of hammering test using topological information
Abstract In this paper we present an online unsupervised method based on clustering to find defects in concrete structures using hammering. First, the initial dataset of sound samples is roughly clustered using the k-means algorithm with the k-means++ seeding procedure in order to find the cluster b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2017-05-01
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| Series: | ROBOMECH Journal |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40648-017-0081-7 |