Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

Chen, Zhangjie; Wang, Mingyi; Wang, Ya

doi:10.1109/jsen.2023.3287565

Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

Journal Article · 01 August 2023 · IEEE Sensors Journal

DOI:https://doi.org/10.1109/jsen.2023.3287565· OSTI ID:2422263

Chen, Zhangjie ^[1]; Wang, Mingyi ^[2]; ^[3]

Department of Mechanical Engineering, Texas A&,M University, College Station, TX, USA; OSTI
Department of Electrical and Computer Engineering, Texas A&,M University, College Station, TX, USA
J. Mike Walker ’,66 Department of Mechanical Engineering, the Department of Electrical and Computer Engineering, and the Department of Biomedical Engineering, Texas A&,M University, College Station, TX, USA

Not provided.

Research Organization:: Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

DOE Contract Number:: AR0000945

OSTI ID:: 2422263

Journal Information:: IEEE Sensors Journal, Journal Name: IEEE Sensors Journal Journal Issue: 15 Vol. 23; ISSN 1530-437X

Publisher:: IEEE

Country of Publication:: United States

Language:: English

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