Author
Listed:
- Tie-Jun Li
- Chih-Cheng Chen
- Jian-jun Liu
- Gui-fang Shao
- Christopher Chun Ki Chan
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.
Suggested Citation
Tie-Jun Li & Chih-Cheng Chen & Jian-jun Liu & Gui-fang Shao & Christopher Chun Ki Chan, 2020.
"A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE,"
Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-9, September.
Handle:
RePEc:hin:jnddns:6787608
DOI: 10.1155/2020/6787608
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