ArticleSensors (Basel, Switzerland)2024
Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency Domain Reflectometry.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Recent Progress in Optical Frequency-Domain Reflectometry: Performance Enhancement, Emerging Applications and Machine Learning Integration.Sensors (Basel, Switzerland) · 2026Review
- A Review of Multiparameter Fiber-Optic Distributed Sensing Techniques for Simultaneous Measurement of Temperature, Strain, and Environmental Effects.Sensors (Basel, Switzerland) · 2025Review
- Comprehensive Analysis of FBG and Distributed Rayleigh, Brillouin, and Raman Optical Sensor-Based Solutions for Road Infrastructure Monitoring Applications.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.