用变分自编码器同时实现传感器校准与数据重建。
Variational Autoencoder for Calibration: A New Approach
- 将潜在空间训练为校准输出，实现端到端校准。
- 模型在重建与校准任务上均表现良好，输出与真实数据统计相似。
- 适用于多传感器数据校准，适合工业传感系统优化。
本文提出一种基于变分自编码器（VAE）的新方法，用于传感器校准。我们建议将潜在空间训练为校准输出，实现端到端校准。通过现有多传感器气体数据集验证了该方法的可行性，结果显示该模型既能作为校准模型，又能作为自编码器使用。其校准输出和重构输出均能生成与真实数据统计上相似的结果。文章还讨论了未来测试方法及该工作的扩展方向。
原文摘要 · Abstract (English)
In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent space as a calibration output. We discuss this new approach and show a proof-of-concept using an existing multi-sensor gas dataset. We show the performance of the proposed calibration VAE and found that it was capable of performing as calibration model while performing as an autoencoder simultaneously. Additionally, these models have shown that they are capable of creating statistically similar outputs from both the calibration output as well as the reconstruction output to their respective truth data. We then discuss the methods of future testing and planned expansion of this work.
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