DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이도헌 | - |
dc.contributor.author | Lee, Sangyeon | - |
dc.contributor.author | 이상연 | - |
dc.date.accessioned | 2024-08-08T19:31:06Z | - |
dc.date.available | 2024-08-08T19:31:06Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1099230&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322018 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2024.2,[v, 80 p. :] | - |
dc.description.abstract | Quantifying the exact amount of a substance in a mixture is a fundamental task in analytical chemistry. With the advantages of high sensitivity, selectivity, immediacy, and non-destructive, spectroscopy is used for quantification. However, there is no universal method to analyze spectral signals for quantification due to the complexity of spectral data. This paper suggested a linear-scaling autoencoder for quantitative analysis with spectral data. Based on the autoencoder architecture, we introduce a modified loss function to align data points in a linear scale on a latent space corresponding to the known quantity labels. The model reduces the dimensionality while preserving the structure of the data and predicts the quantity with the given signal. We validated the model with synthetic and real-world benchmarks and in-house spectroscopy data. The model achieves high performance in quantity prediction and interpretability compared to the existing methodologies. The expandability of the model is also verified using multidimensional data from various fields. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 분광 데이터▼a정량 분석▼a심층 학습▼a오토인코더 | - |
dc.subject | Spectroscopy data▼aQuantitative analysis▼aDeep learning▼aAutoencoder | - |
dc.title | Linear-scaling autoencoders for quantification with multidimensional spectroscopic data | - |
dc.title.alternative | 다차원 분광 정량 분석을 위한 선형 스케일링 오토인코더 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Lee, Doheon | - |
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