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Unsupervised learning approach to automation of hammering test using topological information

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Academic year: 2020

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Figure

Fig. 1 Hammering test conducted by a professional on the ceil-ing portion of a tunnel: only a simple hammer is needed thus the popularity of this non-destructive testing method
Fig. 2 Main steps of our proposed method given a dataset of samples: 1 roughly cluster in order to swiftly make groups of similar sound samples
Fig. 3 Illustration of the proposed weight associated to each sample: a disc centered on the spot hit with the hammer with a radius half of the Euclidian distance to the nearest neighbor
Fig. 4 In this feature vector space where only relative distances are obtainable, the regular model serves as a reference for the whole dataset
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