Real-time Calibration Model for Low-cost Sensor in Fine-grained Time series

• URL: http://arxiv.org/abs/2412.20170v1
• Date: Sat, 28 Dec 2024 14:58:46 GMT
• Title: Real-time Calibration Model for Low-cost Sensor in Fine-grained Time series
• Authors: Seokho Ahn, Hyungjin Kim, Sungbok Shin, Young-Duk Seo,
• Abstract summary: We develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention.<n> TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components.<n>Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency.
• Score: 6.648146664198283
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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