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Metal Oxide Gas Sensors Signal Shape Processing for Selective Detection of Hydrocarbons in Realistic Air Conditionsтезисы доклада Тезисы
- Авторы: Efitorov Alexander, Andreev Matvey, Krivetskiy Valeriy
- Сборник: ECS Meeting Abstracts
- Серия: MA2020-01
- Том: 1
- Тезисы
- Год издания: 2020
- Издательство: ELECTROCHEMICAL SOC INC
- Местоположение издательства: 65 SOUTH MAIN STREET, PENNINGTON, USA,NJ, 08534
- Первая страница: 1860
- Последняя страница: 1861
- DOI: 10.1149/ma2020-01261860mtgabs
- Аннотация: The selective detection with high spatio-temporal resolution of hydrocarbons leakage as a result of pipelines inconsistency is a valid industrial demand [1]. Perspectives are associated with deployment of distributed networks of chemical sensors, transferring data on ambience pollution to the data processing center [2]. Miniature micromachined metal oxide semiconductor gas sensors possess a great perspective of practical use in this regard, however their long term operation in real atmosphere conditions requires improvement [3].In this work we demonstrate improved selectivity of propane vs. methane detection in low concentrations in the ambient air of highly urbanized location by the SnO2-based semiconductor gas sensors with the modulated working temperature, using statistical analysis of "sensor response/sensor working temperature" shape representation. The obtained results of gases discrimination with artificial neural network (ANN) machine learning algorithm, used such pre-treated data as input samples, demonstrate advantages over similar classification methods without signal pre-processing, or pre-processing with previously reported methods of PCA, wavelet transformation and polynomial curve approximation.
- Добавил в систему: Кривецкий Валерий Владимирович