Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model

Kotaro Haneda; Kenei Matsudaira; Hidetoshi Takahashi

doi:10.1109/MEMS49605.2023.10052249

Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model

Kotaro Haneda, Kenei Matsudaira, Hidetoshi Takahashi

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper reports a compact spherical airflow sensor using multi-MEMS differential pressure (DP) sensor elements. Three built-in MEMS sensors simultaneously measure the DP around the spherical housing structure so that the measured DPs are converted into 2D wind direction and speed. The sensor outputs are converted into wind direction and speed by neural network. We attached the calibrated sensor to a toy drone as a demonstration. Then, it was confirmed that the output corresponding to wind direction and speed was measured when a crosswind was applied during flight.

Original language	English
Title of host publication	2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	499-502
Number of pages	4
ISBN (Electronic)	9781665493086
DOIs	https://doi.org/10.1109/MEMS49605.2023.10052249
Publication status	Published - 2023
Event	36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023 - Munich, Germany Duration: 2023 Jan 15 → 2023 Jan 19

Publication series

Name	Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)
Volume	2023-January
ISSN (Print)	1084-6999

Conference

Conference	36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023
Country/Territory	Germany
City	Munich
Period	23/1/15 → 23/1/19

Keywords

Airflow sensor
Differential pressure sensor
Machine learning
Neural network

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Mechanical Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/MEMS49605.2023.10052249

Cite this

Haneda, K., Matsudaira, K., & Takahashi, H. (2023). Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model. In 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023 (pp. 499-502). (Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS); Vol. 2023-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MEMS49605.2023.10052249

Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model. / Haneda, Kotaro; Matsudaira, Kenei; Takahashi, Hidetoshi.
2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 499-502 (Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS); Vol. 2023-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Haneda, K, Matsudaira, K & Takahashi, H 2023, Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model. in 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023. Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS), vol. 2023-January, Institute of Electrical and Electronics Engineers Inc., pp. 499-502, 36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023, Munich, Germany, 23/1/15. https://doi.org/10.1109/MEMS49605.2023.10052249

Haneda K, Matsudaira K, Takahashi H. Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model. In 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 499-502. (Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)). doi: 10.1109/MEMS49605.2023.10052249

Haneda, Kotaro ; Matsudaira, Kenei ; Takahashi, Hidetoshi. / Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model. 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems, MEMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 499-502 (Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)).

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abstract = "This paper reports a compact spherical airflow sensor using multi-MEMS differential pressure (DP) sensor elements. Three built-in MEMS sensors simultaneously measure the DP around the spherical housing structure so that the measured DPs are converted into 2D wind direction and speed. The sensor outputs are converted into wind direction and speed by neural network. We attached the calibrated sensor to a toy drone as a demonstration. Then, it was confirmed that the output corresponding to wind direction and speed was measured when a crosswind was applied during flight.",

keywords = "Airflow sensor, Differential pressure sensor, Machine learning, Neural network",

author = "Kotaro Haneda and Kenei Matsudaira and Hidetoshi Takahashi",

note = "Funding Information: This research was supported by the New Energy and Industrial Technology Development Organization (NEDO). Publisher Copyright: {\textcopyright} 2023 IEEE.; 36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023 ; Conference date: 15-01-2023 Through 19-01-2023",

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Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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