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• W3098699507 abstract "In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor's accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method's outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area-resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM)." @default.
• W3098699507 created "2020-11-23" @default.
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• W3098699507 date "2020-11-09" @default.
• W3098699507 modified "2023-10-02" @default.
• W3098699507 title "Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation" @default.
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• W3098699507 doi "https://doi.org/10.3390/s20216389" @default.
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