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dc.contributor.authorVivancos, Hector Ordonezen_US
dc.contributor.authorLi, Guanyaoen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.date.accessioned2018-08-21T05:56:24Z-
dc.date.available2018-08-21T05:56:24Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2376-6816en_US
dc.identifier.urihttp://hdl.handle.net/11536/146164-
dc.description.abstractRecently, PM2.5 becomes one critical threat for the health of human. To monitor the PM2.5, the Airbox project that consists of more than 2000 PM2.5 sensors is executing in Taiwan. Thanks to the Airbox sensors, people can know the fine-grained air quality. However. Airbox sensors can fail and it is important to detect which sensor is failed to prevent the noise in the data. In this work, we focus on fault detection and value estimation for PM2.5 monitoring. To achieve our goal, we utilize the data from Environmental Protection Administration(EPA) for estimation. We firstly propose two estimation methods which consider the distance and similarity between the Airbox sensors and EPA monitoring stations for PM2.5 estimation. Then based on the estimation result, we detect which sensors is failed. We collect the data from Airhox Edimax web page and Taiwan's Environmental Protection Administration for our experiment. The experiment results reveal the good performance of our proposed methods.en_US
dc.language.isoen_USen_US
dc.titleEstimating the Fine-Grained PM2.5 for Airbox Sensor Fault Detection in Taiwanen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)en_US
dc.citation.spage54en_US
dc.citation.epage57en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000434087700014en_US
Appears in Collections:Conferences Paper