research-article

Sensor Drift Calibration via Spatial Correlation Model in Smart Building

Authors:

Bei YuAuthors Info & Claims

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

Article No.: 105, Pages 1 - 6

https://doi.org/10.1145/3316781.3317909

Published: 02 June 2019 Publication History

Abstract

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-aposteriori (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.

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Cited By

Sun QChen TLiu SChen JYu HYu B(2022)Correlated Multi-objective Multi-fidelity Optimization for HLS Directives DesignACM Transactions on Design Automation of Electronic Systems10.1145/350354027:4(1-27)Online publication date: 8-Mar-2022
https://dl.acm.org/doi/10.1145/3503540
Chen TDuan BSun QZhang MLi GGeng HZhang QYu B(2022)An Efficient Sharing Grouped Convolution via Bayesian LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308490033:12(7367-7379)Online publication date: Dec-2022
https://doi.org/10.1109/TNNLS.2021.3084900
Geng HMa YXu QMiao JRoy SYu B(2022)High-Speed Adder Design Space Exploration via Graph Neural ProcessesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.311426241:8(2657-2670)Online publication date: Aug-2022
https://doi.org/10.1109/TCAD.2021.3114262
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cover image ACM Conferences

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

1378 pages

ISBN:9781450367257

DOI:10.1145/3316781

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 June 2019

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DAC '19: The 56th Annual Design Automation Conference 2019

June 2 - 6, 2019

NV, Las Vegas, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Sun QChen TLiu SChen JYu HYu B(2022)Correlated Multi-objective Multi-fidelity Optimization for HLS Directives DesignACM Transactions on Design Automation of Electronic Systems10.1145/350354027:4(1-27)Online publication date: 8-Mar-2022
https://dl.acm.org/doi/10.1145/3503540
Chen TDuan BSun QZhang MLi GGeng HZhang QYu B(2022)An Efficient Sharing Grouped Convolution via Bayesian LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308490033:12(7367-7379)Online publication date: Dec-2022
https://doi.org/10.1109/TNNLS.2021.3084900
Geng HMa YXu QMiao JRoy SYu B(2022)High-Speed Adder Design Space Exploration via Graph Neural ProcessesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.311426241:8(2657-2670)Online publication date: Aug-2022
https://doi.org/10.1109/TCAD.2021.3114262
Chen TSun QZhan CLiu CYu HYu B(2022)Deep H-GCN: Fast Analog IC Aging-Induced Degradation EstimationIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.310725041:7(1990-2003)Online publication date: Jul-2022
https://doi.org/10.1109/TCAD.2021.3107250
Chen TLin BGeng HHu SYu B(2021)Leveraging Spatial Correlation for Sensor Drift Calibration in Smart BuildingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.301543840:7(1273-1286)Online publication date: Jul-2021
https://doi.org/10.1109/TCAD.2020.3015438
Yang AWang PYang H(2021) In Situ Blind Calibration of Sensor Networks for Infrastructure Monitoring IEEE Sensors Journal10.1109/JSEN.2021.310927821:21(24274-24284)Online publication date: 1-Nov-2021
https://doi.org/10.1109/JSEN.2021.3109278
Chen TSun QYu B(2021)Machine Learning in Nanometer AMS Design-for-Reliability : (Invited Paper)2021 IEEE 14th International Conference on ASIC (ASICON)10.1109/ASICON52560.2021.9620496(1-4)Online publication date: 26-Oct-2021
https://doi.org/10.1109/ASICON52560.2021.9620496

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