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深層学習を用いた屋外環境のガス源探索―入力データ長短縮と風速データ平滑化の効果―

深層学習を用いた屋外環境のガス源探索―入力データ長短縮と風速データ平滑化の効果―

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カテゴリ: 論文誌(論文単位)

グループ名: 【E】センサ・マイクロマシン部門

発行日: 2023/11/01

タイトル(英語): Gas Source Localization in Outdoor Environment Using Deep Learning: Effects of Data Length Reduction and Wind Data Smoothing

著者名: 趙 高挙(東京農工大学生物システム応用科学府),坂上 源生(東京農工大学生物システム応用科学府),松倉 悠(電気通信大学情報理工学研究科),石田 寛(東京農工大学生物システム応用科学府)

著者名(英語): Gao-ju Zhao (Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology), Motoki Sakaue (Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology), Haruka Matsukura (Graduate School of Informatics and Engineering, University of Electro-Communications), Hiroshi Ishida (Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology)

キーワード: ガス源探索,ガスセンサ,風向風速計,深層学習,ガス,気流  gas source localization,gas sensor,anemometer,deep learning,gas,airflow

要約(英語): The aim of this research project is to attain accurate gas-source localization in outdoor environments with large wind fluctuations. For this purpose, we propose to use a long short-term memory deep-learning framework to time-series data collected by a sensor network consisting of multiple gas sensors and an anemometer. This paper describes impacts of the length of time-series data and smoothing of wind data provided to a deep neural network model. We have collected three datasets by placing 30 semiconductor gas sensors and one ultrasonic anemometer in an outdoor field in different seasons. We have found that the success rate of gas-source location estimation can be effectively increased by removing high frequency fluctuations in the time-series data of the wind velocity vector by taking moving average before applying the data to the neural network. By adjusting the data length provided to the neural network and smoothing the wind data, the success rate of gas-source location estimation has been increased from 82.5% to 86.7%. A success rate of 78.8% has been obtained even when half of the gas sensors have been removed.

本誌: 電気学会論文誌E(センサ・マイクロマシン部門誌) Vol.143 No.11 (2023) 特集:匂い・味に関するセンシングおよび提示技術

本誌掲載ページ: 357-364 p

原稿種別: 論文/日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejsmas/143/11/143_357/_article/-char/ja/

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