Deep Learning Algorithms for Chemometric Analysis in MIR Gas Spectroscopy
dc.contributor.author | Chin, Sanghoon | |
dc.contributor.author | Van Zaen, Jerome | |
dc.contributor.author | Muntane, Enric | |
dc.contributor.author | Denis, Severine | |
dc.contributor.author | Lecomte, Steve | |
dc.contributor.author | Balet, Laurent | |
dc.date.accessioned | 2024-10-22T12:35:17Z | |
dc.date.available | 2024-10-22T12:35:17Z | |
dc.date.issued | 2024-07-17 | |
dc.description.abstract | We have implemented machine learning techniques into a mid-infrared gas spectrometer for two specific goals: the improvement of chemometric analysis using artificial neural networks and geostatistical analysis over a geographic area using Kriging. | |
dc.description.sponsorship | This project has received funding from Horizon 2020, the European Union’s Framework Program for Research and Innovation, under grant agreement No.101015825 (TRIAGE). | |
dc.identifier.citation | Optica Sensing Congress 2024 (AIS, LACSEA, Sensors, QSM) | |
dc.identifier.uri | https://hdl.handle.net/20.500.12839/1530 | |
dc.language.iso | en | |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.title | Deep Learning Algorithms for Chemometric Analysis in MIR Gas Spectroscopy | |
dc.type | Conference | |
dc.type.csemdivisions | BU-I | |
dc.type.csemresearchareas | Photonics | |
dc.type.csemresearchareas | Scientific Instrumentation |
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