ArticleSensors (Basel, Switzerland)2023
Machine-Learning-Based Calibration of Temperature Sensors.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Structurally Robust Framework for Intelligent Graphene Thermometry via Few-Shot Transfer Learning and Algorithm-Hardware Co-Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI Methods in Sensor Calibration.Sensors (Basel, Switzerland) · 2026Review
- Deep Learning-Based Calibration of a Multi-Point Thin-Film Thermocouple Array for Temperature Field Measurement.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning Calibration of Smartphone-Based Infrared Thermal Cameras: Improved Bias and Persistent Random Error.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning-Driven Sensitivity Analysis for a 2-Layer Printed Circuit Board Inductive Motor Position Sensor.Sensors (Basel, Switzerland) · 2026Article
- Gaussian Process-Based Multi-Fidelity Bayesian Optimization for Optimal Calibration Point Selection.Sensors (Basel, Switzerland) · 2025Article
- Temperature Calibration Using Machine Learning Algorithms for Flexible Temperature Sensors.Sensors (Basel, Switzerland) · 2025Article
- A high-precision 1 × 15 infrared temperature measurement linear array based on thermopile sensors.Communications engineering · 2025Article
- Machine Learning-Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors.Sensors (Basel, Switzerland) · 2025Article
- Distributed Sensor Network Calibration Under Sensor Nonlinearities with Applications in Aerodynamic Pressure Sensing.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Transducer for Temperature Measurement with Two-Wire or Three-Wire Platinum RTD.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.
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