Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration.
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| Title: | Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration. |
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| 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] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177892462 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Julian%2C+James%22">Julian, James</searchLink><relatesTo>1</relatesTo><i> zames@upnvj.ac.id</i><br /><searchLink fieldCode="AR" term="%22Wahyuni%2C+Fitri%22">Wahyuni, Fitri</searchLink><relatesTo>1</relatesTo><i> annastya.bd@upnvj.ac.id</i><br /><searchLink fieldCode="AR" term="%22Dewantara%2C+Annastya+Bagas%22">Dewantara, Annastya Bagas</searchLink><relatesTo>2</relatesTo><i> fitriwahyuni@upnvj.ac.id</i><br /><searchLink fieldCode="AR" term="%22Winarta%2C+Adi%22">Winarta, Adi</searchLink><relatesTo>3</relatesTo><i> adi.winarta@pnb.ac.id</i><br /><searchLink fieldCode="AR" term="%22Putra%2C+Nandy%22">Putra, Nandy</searchLink><relatesTo>4</relatesTo><i> nandyputra@eng.ui.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2024, Vol. 14 Issue 3, p2625-2633. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22National+Instruments+Corp%2E%22">National Instruments Corp.</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+noise%22">Thermal noise</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Electromagnetic+interference%22">Electromagnetic interference</searchLink><br /><searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Thermocouples%22">Thermocouples</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v14i3.pp2625-2633 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 2625 Subjects: – SubjectFull: National Instruments Corp. Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Thermal noise Type: general – SubjectFull: Wavelet transforms Type: general – SubjectFull: Calibration Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Electromagnetic interference Type: general – SubjectFull: Image denoising Type: general – SubjectFull: Thermocouples Type: general Titles: – TitleFull: Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Julian, James – PersonEntity: Name: NameFull: Wahyuni, Fitri – PersonEntity: Name: NameFull: Dewantara, Annastya Bagas – PersonEntity: Name: NameFull: Winarta, Adi – PersonEntity: Name: NameFull: Putra, Nandy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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