A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/6787608 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850163878294454272 |
|---|---|
| author | Tie-Jun Li Chih-Cheng Chen Jian-jun Liu Gui-fang Shao Christopher Chun Ki Chan |
| author_facet | Tie-Jun Li Chih-Cheng Chen Jian-jun Liu Gui-fang Shao Christopher Chun Ki Chan |
| author_sort | Tie-Jun Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9770e962f724e89a92c28dfae8acc4f |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-e9770e962f724e89a92c28dfae8acc4f2025-08-20T02:22:06ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/67876086787608A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNETie-Jun Li0Chih-Cheng Chen1Jian-jun Liu2Gui-fang Shao3Christopher Chun Ki Chan4College of Information Engineering, Jimei University, Fujian Province, Xiamen 361021, ChinaCollege of Information Engineering, Jimei University, Fujian Province, Xiamen 361021, ChinaShaoguan University, Guangzhou Province, Shaoguan 512005, ChinaDepartment of Automation, Xiamen University, Fujian Province, Xiamen 361005, ChinaDepartment of Information Management, Chaoyang University of Technology, Taichung, TaiwanWe 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.http://dx.doi.org/10.1155/2020/6787608 |
| spellingShingle | Tie-Jun Li Chih-Cheng Chen Jian-jun Liu Gui-fang Shao Christopher Chun Ki Chan A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE Discrete Dynamics in Nature and Society |
| title | A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE |
| title_full | A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE |
| title_fullStr | A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE |
| title_full_unstemmed | A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE |
| title_short | A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE |
| title_sort | novel thz differential spectral clustering recognition method based on t sne |
| url | http://dx.doi.org/10.1155/2020/6787608 |
| work_keys_str_mv | AT tiejunli anovelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT chihchengchen anovelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT jianjunliu anovelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT guifangshao anovelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT christopherchunkichan anovelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT tiejunli novelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT chihchengchen novelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT jianjunliu novelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT guifangshao novelthzdifferentialspectralclusteringrecognitionmethodbasedontsne AT christopherchunkichan novelthzdifferentialspectralclusteringrecognitionmethodbasedontsne |