Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

► This study proposes an evolution strategy to perform gas sensor drift correction. ► The correction is learnt without hypothesis on the behavior of the sensors. ► The proposed approach transparently adapts to changes in the sensors’ response. Artificial olfaction systems, which mimic human olfactio...

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Vydáno v:	Pattern recognition letters Ročník 32; číslo 13; s. 1594 - 1603
Hlavní autoři:	Di Carlo, S., Falasconi, M., Sanchez, E., Scionti, A., Squillero, G., Tonda, A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.10.2011 Elsevier
Témata:	Applied sciences Biological and medical sciences Classification systems Computer Science Computerized, statistical medical data processing and models in biomedicine Electronics Engineering Sciences Evolutionary strategy Exact sciences and technology Information, signal and communications theory Machine Learning Medical management aid. Diagnosis aid Medical sciences Neural and Evolutionary Computing Pattern recognition Sensor drift Signal processing Telecommunications and information theory Evolutionary strategy Sensor drift Classification systems State of the art Iterative method Pattern recognition Chemical sensor Adaptive method Accuracy Optimal strategy Linear transformation Safety Diagnostic aid Environmental monitoring Cost lowering classification systems sensor drift evolutionary strategy
ISSN:	0167-8655, 1872-7344
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Abstract	► This study proposes an evolution strategy to perform gas sensor drift correction. ► The correction is learnt without hypothesis on the behavior of the sensors. ► The proposed approach transparently adapts to changes in the sensors’ response. Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor’s stability over time, still limits real industrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors’ measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors.
AbstractList	Artificial olfaction systems; which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real industrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors. ► This study proposes an evolution strategy to perform gas sensor drift correction. ► The correction is learnt without hypothesis on the behavior of the sensors. ► The proposed approach transparently adapts to changes in the sensors’ response. Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor’s stability over time, still limits real industrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors’ measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors.
Author	Scionti, A. Di Carlo, S. Sanchez, E. Tonda, A. Squillero, G. Falasconi, M.
Author_xml	– sequence: 1 givenname: S. surname: Di Carlo fullname: Di Carlo, S. email: stefano.dicarlo@polito.it organization: Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy – sequence: 2 givenname: M. surname: Falasconi fullname: Falasconi, M. organization: SENSOR CNR-IDASC, Brescia, Italy – sequence: 3 givenname: E. surname: Sanchez fullname: Sanchez, E. organization: Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy – sequence: 4 givenname: A. surname: Scionti fullname: Scionti, A. organization: Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy – sequence: 5 givenname: G. surname: Squillero fullname: Squillero, G. organization: Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy – sequence: 6 givenname: A. surname: Tonda fullname: Tonda, A. organization: Politecnico di Torino, Control and Computer Engineering Department, Torino, Italy
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Keywords	Evolutionary strategy Sensor drift Classification systems State of the art Iterative method Pattern recognition Chemical sensor Adaptive method Accuracy Optimal strategy Linear transformation Safety Diagnostic aid Environmental monitoring Cost lowering classification systems sensor drift evolutionary strategy
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Snippet	► This study proposes an evolution strategy to perform gas sensor drift correction. ► The correction is learnt without hypothesis on the behavior of the... Artificial olfaction systems; which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a...
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SubjectTerms	Applied sciences Biological and medical sciences Classification systems Computer Science Computerized, statistical medical data processing and models in biomedicine Electronics Engineering Sciences Evolutionary strategy Exact sciences and technology Information, signal and communications theory Machine Learning Medical management aid. Diagnosis aid Medical sciences Neural and Evolutionary Computing Pattern recognition Sensor drift Signal processing Telecommunications and information theory
Title	Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation
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