Name
Title | ||
|---|---|---|
Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.patrec.2011.05.019 | Pattern Recognition Letters |
Keywords | Field | DocType |
classification systems,raw sensor,adaptive drift-correction method,evolutionary strategy,pattern recognition method,artificial olfaction system,pattern recognition accuracy,human olfaction,optimal correction strategy,drift compensation,sensor drift,gas chemical sensor,cutting-edge evolutionary strategy,physical chemical sensor,linear transformation,health and safety,pattern recognition,process control,classification system | Electronic nose,Pattern recognition,Exploit,Evolution strategy,Clinical diagnosis,Artificial intelligence,Process control,Machine learning,Environmental monitoring,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 13 | Pattern Recognition Letters |
Citations | PageRank | References |
5 | 0.63 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Di Carlo | 1 | 128 | 9.63 |
| M. Falasconi | 2 | 17 | 1.33 |
| E. Sanchez | 3 | 130 | 16.50 |
| Alberto Scionti | 4 | 60 | 11.97 |
| G. Squillero | 5 | 330 | 30.36 |
| Alberto Paolo Tonda | 6 | 127 | 20.85 |