A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE

Tie-Jun Li, Chih-Cheng Chen, Jian-jun Liu, Gui-fang Shao and Christopher Chun Ki Chan

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-9

Abstract:

We apply time-domain spectroscopy (THz) imaging technology to perform nondestructive detection on three industrial ceramic matrix composite (CMC) samples and one silicon slice with defects. In terms of spectrum recognition, a low-resolution THz spectrum image results in an ineffective recognition on sample defect features. Therefore, in this article, we propose a spectrum clustering recognition model based on t-distribution stochastic neighborhood embedding (t-SNE) to address this ineffective sample defect recognition. Firstly, we propose a model to recognize a reduced dimensional clustering of different spectrums drawn from the imaging spectrum data sets, in order to judge whether a sample includes a feature indicating a defect or not in a low-dimensional space. Second, we improve computation efficiency by mapping spectrum data samples from high-dimensional space to low-dimensional space by the use of a manifold learning algorithm (t-SNE). Finally, to achieve a visible observation of sample features in low-dimensional space, we use a conditional probability distribution to measure the distance invariant similarity. Comparative experiments indicate that our model can judge the existence of sample defect features or not through spectrum clustering, as a predetection process for image analysis.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6787608

DOI: 10.1155/2020/6787608

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