2026-04-13T16:53:17Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1135812025-07-17T04:45:06Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Rodriguez Lujan, Irene author Fonollosa Magrinyà, Jordi author Huerta, Ramon author 2016 The main existent tool to monitor chemical environ- ments in a continuous mode is gas sensor arrays, which have been popularized as electronic noses (enoses). To design and validate these monitoring systems, it is necessary to make use of machine learning techniques to deal with large amounts of heterogeneous data and extract useful information from them. Therefore, enose data present several challenges for each of the steps involved in the design of a machine learning system. Some of the machine learning tasks involved in this area of research include generation of operational patterns, detection anomalies, or classification and discrimination of events. In this work, we will review some of the machine learning approaches adopted in the literature for enose data analysis, and their application to three different tasks: single gas classification under tightly-controlled operating conditions, gas binary mixtures classification in a wind tunnel with two independent gas sources, and human activity monitoring in a NASA spacecraft cabin simulator. Postprint (author's final draft) Àrees temàtiques de la UPC::Enginyeria electrònica Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors Chemical detectors Nas electrònic Sensors químics Detectors Machine learning methods in electronic nose analysis