Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of od...

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Bibliographic Details
Main Authors: Sabina Licen, Alessia Di Gilio, Jolanda Palmisani, Stefania Petraccone, Gianluigi de Gennaro, Pierluigi Barbieri
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Sensors
Subjects:
electronic nose
ambient air
oil and gas
self-organizing map
neural networks
Online Access:https://www.mdpi.com/1424-8220/20/7/1887

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https://www.mdpi.com/1424-8220/20/7/1887

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