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- Title
Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration.
- Authors
Julian, James; Wahyuni, Fitri; Dewantara, Annastya Bagas; Winarta, Adi; Putra, Nandy
- Abstract
Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices.
- Subjects
NATIONAL Instruments Corp.; DEEP learning; THERMAL noise; WAVELET transforms; CALIBRATION; SIGNAL-to-noise ratio; ELECTROMAGNETIC interference; IMAGE denoising; THERMOCOUPLES
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2024, Vol 14, Issue 3, p2625
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v14i3.pp2625-2633