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Using machine learning methods for joint processing of data from multiple semiconductor gas sensorsтезисы доклада
- Авторы: Isaev I.V., Chernov K.N., Dolenko S.A., Krivetskiy V.V.
- Сборник: DLCP Abstracts
- Тезисы
- Год издания: 2025
- Аннотация: This study addresses the problem of environmental monitoring of air in cities and industrial areas, which consists in detecting gases and volatile organic compounds using semiconductor gas sensors. To provide selectivity in the detection of certain gases, as well as high temporal resolution of the sensors, nonlinear temperature operating conditions were used - the so-called heating dynamics. Due to high complexity of physical and chemical models describing the processes of interaction between gases and sensors, machine learning methods based on the use of physical experiment data were used to process the sensor response. To provide additional selectivity in the detection of specific gases, this study considers simultaneous use of data from multiple semiconductor sensors with various doping components with building machine learning models capable of providing joint processing. Based on the results of the study, conclusions were made regarding the selection of optimal combinations of sensors and heating dynamics for a specific gas/all gases. The study was carried out at the expense of the grant No. 22-19-00703-P from the Russian Science Foundation.
- Добавил в систему: Исаев Игорь Викторович