Using PCA and DNN for fitting raw TDLAS spectra : application to a supersonic flow
Overview & Synopsis
A novel method is described for fitting raw Tunable Diode Laser Absorption Spectroscopy (TDLAS) spectra leveraging Principal Component Analysis (PCA) in conjunction with Deep Neural Networks (DNN). It showed promising results for weakly absorbing transitions.
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hal.science)
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Document Information
| Document Title | Using PCA and DNN for fitting raw TDLAS spectra : application to a supersonic flow |
|---|---|
| Author / Organization | Gautier Vilmart |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2024 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-72435 |
| Original Source Link | Visit Source Page on hal.science » |