Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency Domain Reflectometry
Mateusz Mądry, Bogusław Szczupak, Mateusz Śmigielski, Bartosz Matysiak · 2024
Read Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency Domain Reflectometry by Mateusz Mądry, Bogusław Szczupak, Mateusz Śmigielski, Bartosz Matysiak (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11679379.txt.
Description
Mateusz Mądry, Bogusław Szczupak, Mateusz Śmigielski, and Bartosz Matysiak present a 2024 sensor study. Their device measures heat and damp air at the same time, a pair that is hard to split with one probe. The sensor uses light sent through thin glass fibers. One fiber is bare, and one has a coat that reacts to damp air.
A machine learning model reads the light signals and sorts out the two values. Tests gave small errors for both heat and damp air over a short sensor length. The authors also checked how sensor length and data size change the errors. The method skips slow hand work and may suit sites that need both readings at once.
Catalog record
| CREATOR | Mateusz Mądry, Bogusław Szczupak, Mateusz Śmigielski, Bartosz Matysiak |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 26 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |