用Transformer模型实现低成本传感器的实时精准校准
Real-time Calibration Model for Low-cost Sensor in Fine-grained Time series
- 基于对数分箱注意力机制，降低计算复杂度
- 在资源受限设备上保持高精度与实时性，序列越长效果越优
- 适合嵌入式系统、物联网等低功耗场景的传感器校准
高精度传感器测量至关重要，但实际中常使用低成本、低技术的采集系统，数据往往不准确，需进一步校准。为此，我们首先提出三种适用于实际低技术传感器环境的有效校准要求。基于这些要求，开发了名为 TESLA（Transformer for effective sensor calibration utilizing logarithmic-binned attention）的模型。TESLA 利用高性能深度学习模型 Transformer 来捕捉非线性特征并实现校准。其核心采用对数分箱策略，显著降低注意力机制的计算复杂度。实验表明，TESLA 在硬件受限系统中仍能实现一致的实时校准，即使面对更长序列和细粒度时间序列亦表现优异。相比现有先进的深度学习模型和新设计的线性模型，TESLA 在准确性、校准速度和能效方面均取得更优表现。
原文摘要 · Abstract (English)
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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