首页|Massey University Researcher Updates Current Study Findings on Machine Learning (Leveraging Temporal Information to Improve Machine Learning-Based Calibration T echniques for Low-Cost Air Quality Sensors)
Massey University Researcher Updates Current Study Findings on Machine Learning (Leveraging Temporal Information to Improve Machine Learning-Based Calibration T echniques for Low-Cost Air Quality Sensors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Auckland, N ew Zealand, by NewsRx editors, research stated, “Low-cost ambient sensors have b een identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution.” The news correspondents obtained a quote from the research from Massey Universit y: “However, the pollutant data captured by these cost-effective sensors are les s accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed a nd the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambie nt gas pollutant sensors.”
Massey UniversityAucklandNew ZealandAustralia and New ZealandCyborgsEmerging TechnologiesMachine Learning
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