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Odor source identification by grounding linguistic descriptions in an artificial nose
Örebro universitet, Institutionen för teknik. (Mobile Robotics Lab)ORCID-id: 0000-0002-3122-693X
Örebro universitet, Institutionen för teknik. (Mobile Robotics Lab)
Örebro universitet, Institutionen för teknik. (Learning Systems Lab)
Örebro universitet, Institutionen för teknik. (Mobile Robotics Lab)
2001 (engelsk)Inngår i: Proceedings of the the sensor fusion conference: architectures, algorithms, and applications, 2001, s. 273-281Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different selectivity, the outputs of which are fused together with an artificial neural network in order to recognize various human-determined odors. The basic idea is to ground human-provided linguistic descriptions of these odors in the actual sensory perceptions of the nose through a process of supervised learning. Analogous to the human nose, the paper explains a method by which an electronic nose can be used for substance identification. First, the receptors of the nose are exposed to a substance by means of inhalation with an electric pump. Then a chemical reaction takes place in the gas sensors over a period of time and an artificial neural network processes the resulting sensor patterns. This network was trained to recognize a basic set of pure substances such as vanilla, lavender and yogurt under controlled laboratory conditions. The complete system was then validated through a series of experiments on various combinations of the basic substances. First, we showed that the nose was able to consistently recognize unseen samples of the same substances on which it had been trained. In addition, we presented some first results where the nose was tested on novel combinations of substances on which it had not been trained by combining the learned descriptions - for example, it could distinguish lavender yogurt as a combination of lavender and yogurt.

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2001. s. 273-281
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URN: urn:nbn:se:oru:diva-3938DOI: 10.1117/12.421115ISI: 000169941900028OAI: oai:DiVA.org:oru-3938DiVA, id: diva2:138237
Konferanse
Sensor Fusion Conference: Architectures, Algorithms, and Applications, SPIE, Orlando, FL, 2001
Tilgjengelig fra: 2007-08-15 Laget: 2007-08-15 Sist oppdatert: 2022-11-25bibliografisk kontrollert

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Loutfi, AmyCoradeschi, SilviaDuckett, TomWide, Peter

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