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- W4310608281 abstract "Low-cost particulate matter (LC-PM) sensors have been studied around the world as a viable alternative to expensive reference stations for monitoring air quality. However, LC-PM sensors require periodic calibration, since their data are often inaccurate and subject to uncertainty. Sensors calibration can be performed through machine learning methods where the sensor is placed in a real environment subject to the local environmental conditions of the place and its measurement compared to a reference equipment. This work evaluates different machine learning methods in five different models of LC-PM sensors, aiming to select the most appropriate sensor and a calibration method to be used in a low-cost air quality station in the context of smart cities." @default.
- W4310608281 created "2022-12-13" @default.
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- W4310608281 date "2022-11-21" @default.
- W4310608281 modified "2023-09-27" @default.
- W4310608281 title "A Machine Learning-Based Approach to Calibrate Low-Cost Particulate Matter Sensors" @default.
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- W4310608281 doi "https://doi.org/10.1109/sbesc56799.2022.9964983" @default.
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