A LSTM-based QCM Gas Sensor Transient Response Model for Binary Gas Mixture

カテゴリ：部門大会

論文No：11A3-PS-88

グループ名：【E】第42回「センサ・マイクロマシンと応用システム」シンポジウム

発行日：2025/11/3

タイトル(英語)：A LSTM-based QCM Gas Sensor Transient Response Model for Binary Gas Mixture

著者名：*Bao Ziteng（Institute of Science Tokyo）, Aleixandre Manuel（Institute of Science Tokyo）, Hasegawa Shoichi（Institute of Science Tokyo）, Nakamoto Takamichi（Institute of Science Tokyo）

著者名(英語)：

キーワード：Neural network,LSTM,QCM gas sensor,transient response,gas mixture,Neural network,LSTM,QCM gas sensor,transient response,gas mixture

要約(日本語)：This study employed a LSTM neural network to predict the transient responses of a QCM gas sensor to dynamically varying binary gas mixtures and optimized its hyperparameters and structure. The results confirmed that it has good prediction accuracy and significantly outperforms the linear model.

要約(英語)：This study employed a LSTM neural network to predict the transient responses of a QCM gas sensor to dynamically varying binary gas mixtures and optimized its hyperparameters and structure. The results confirmed that it has good prediction accuracy and significantly outperforms the linear model.

PDFファイルサイズ：194Kバイト