Real-Time Calibration Model for Low-Cost Sensor in Fine-Grained Time Series
Seokho Ahn, Hyungjin Kim, Sungbok Shin, Young-Duk Seo
Abstract
Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning, to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency.
BibTeX
@article{Ahn_Kim_Shin_Seo_2025, title={Real-Time Calibration Model for Low-Cost Sensor in Fine-Grained Time Series}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/31974}, DOI={10.1609/aaai.v39i1.31974}, abstractNote={Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning, to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ahn, Seokho and Kim, Hyungjin and Shin, Sungbok and Seo, Young-Duk}, year={2025}, month={Apr.}, pages={3-11} }