Calibration of a Low-Cost Methane Sensor Using Machine Learning.

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.

By which name should I call thee? The consequences of having multiple names.

The nominal competitor effect suggests that, when a person has two names associated with them, recall of either name is more difficult than if they just had one name. Drawing on a connectionist framework, this effect could arise either if multiple names were represented as being connected to a single person identity node (PIN), or if multiple names were represented as being connected via one-to-one links to multiple PINs. Whilst the latter has intuitive appeal, results from two experiments support the former architecture. Having two names connected to a single PIN not only gives rise to a nominal competitor effect (Experiment 1), but also gives rise to a familiarity enhancement effect (Experiment 2). These empirical results are simulated using an extension of Brédart, Valentine, Calder, and Gassi's (1995) connectionist architecture, which reveals that both effects hold even when the association of both names to the PIN is unequal. These results are presented in terms of a more complete model for person recognition, and the representation of semantic information within such a model is examined.