TY - GEN
T1 - A novel approach for gas discrimination in natural environments with open sampling systems
AU - Bennetts, Victor Hernandez
AU - Schaffernicht, Erik
AU - Sesé, Victor Pomareda
AU - Lilienthal, Achim J.
AU - Trincavelli, Marco
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/12
Y1 - 2014/12/12
N2 - This work presents a gas discrimination approach for Open Sampling Systems (OSS), composed of non-specific metal oxide sensors only. In an OSS, as used on robots or in sensor networks, the sensors are exposed to the dynamics of the environment and thus, most of the data corresponds to highly diluted samples while high concentrations are sparse. In addition, a positive correlation between class separability and concentration level can be observed. The proposed approach computes the class posteriors by coupling the pairwise probabilities between the compounds to a confidence model based on an estimation of the concentration. In this way a rejection posterior, analogous to the detection limit of the human nose, is learned. Evaluation was conducted in indoor and outdoor sites, with an OSS equipped robot, in the presence of two gases. The results show that the proposed approach achieves a high classification performance with a low sensitivity to the selection of meta parameters.
AB - This work presents a gas discrimination approach for Open Sampling Systems (OSS), composed of non-specific metal oxide sensors only. In an OSS, as used on robots or in sensor networks, the sensors are exposed to the dynamics of the environment and thus, most of the data corresponds to highly diluted samples while high concentrations are sparse. In addition, a positive correlation between class separability and concentration level can be observed. The proposed approach computes the class posteriors by coupling the pairwise probabilities between the compounds to a confidence model based on an estimation of the concentration. In this way a rejection posterior, analogous to the detection limit of the human nose, is learned. Evaluation was conducted in indoor and outdoor sites, with an OSS equipped robot, in the presence of two gases. The results show that the proposed approach achieves a high classification performance with a low sensitivity to the selection of meta parameters.
UR - http://www.scopus.com/inward/record.url?scp=84931057752&partnerID=8YFLogxK
U2 - 10.1109/ICSENS.2014.6985437
DO - 10.1109/ICSENS.2014.6985437
M3 - Conference contribution
AN - SCOPUS:84931057752
T3 - Proceedings of IEEE Sensors
SP - 2046
EP - 2049
BT - IEEE SENSORS 2014, Proceedings
A2 - Arregui, Francisco J.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE SENSORS Conference, SENSORS 2014
Y2 - 2 November 2014 through 5 November 2014
ER -