Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration.

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Bibliographic Details
Title:	Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration.
Authors:	Julian, James¹ zames@upnvj.ac.id, Wahyuni, Fitri¹ annastya.bd@upnvj.ac.id, Dewantara, Annastya Bagas² fitriwahyuni@upnvj.ac.id, Winarta, Adi³ adi.winarta@pnb.ac.id, Putra, Nandy⁴ nandyputra@eng.ui.ac.id
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Jun2024, Vol. 14 Issue 3, p2625-2633. 9p.
Subjects:	National Instruments Corp., Deep learning, Thermal noise, Wavelet transforms, Calibration, Signal-to-noise ratio, Electromagnetic interference, Image denoising, Thermocouples
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v14i3.pp2625-2633