Sensor Drift Calibration via Spatial Correlation Model in Smart Building
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員
替代計量分析
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其它資訊
摘要Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this paper, we propose a sensor spatial correlation model. Given prior knowledge, Maximum-a-posteriori (MAP) estimation is performed to calibrate drifts. MAP is formulated as a non-convex problem with three hyper-parameters. An alternating-based method is proposed to solve this non-convex formulation. Cross-validation and Expectation-maximum with Gibbs sampling are further to determine hyper-parameters. Experimental results show that on benchmarks from simulator EnergyPlus, compared with state-of-the-art method, the proposed framework can achieve a robust drift calibration and a better trade-off between accuracy and runtime.
著者Tinghuan Chen, Bingqing Lin, Hao Geng, Bei Yu
會議名稱56th ACM/EDAC/IEEE Design Automation Conference (DAC)
會議開始日02.06.2019
會議完結日06.06.2019
會議地點Las Vegas, NV
會議國家/地區美國
會議論文集題名DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019
出版年份2019
國際標準書號978-1-4503-6725-7
語言美式英語

上次更新時間 2020-18-01 於 03:16