Affiliation:
1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Abstract
Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.
Funder
National Science Foundation of China
Reference27 articles.
1. Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities;Berger;IEEE Sens. J.,2023
2. Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance;Bulot;Heliyon,2023
3. Munz, H., Ingwersen, J., and Streck, T. (2023). On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements. Sensors, 23.
4. You Are Sensing, but Are You Biased? A User Unaided Sensor Calibration Approach for Mobile Sensing;Grammenos;Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.,2018
5. Intelligent Air Pollution Sensors Calibration for Extreme Events and Drifts Monitoring;Zaidan;IEEE Trans. Indust. Inf.,2023