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Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose

School of Microelectronics and Communication Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing 400044, China
Authors to whom correspondence should be addressed.
Sensors 2018, 18(11), 4028;
Received: 19 October 2018 / Revised: 8 November 2018 / Accepted: 12 November 2018 / Published: 19 November 2018
(This article belongs to the Special Issue Electronic Noses and Their Application)
Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work. View Full-Text
Keywords: electronic nose; drift counteraction; active learning; online electronic nose; drift counteraction; active learning; online
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MDPI and ACS Style

Liu, T.; Li, D.; Chen, J.; Chen, Y.; Yang, T.; Cao, J. Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose. Sensors 2018, 18, 4028.

AMA Style

Liu T, Li D, Chen J, Chen Y, Yang T, Cao J. Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose. Sensors. 2018; 18(11):4028.

Chicago/Turabian Style

Liu, Tao, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang, and Jianhua Cao. 2018. "Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose" Sensors 18, no. 11: 4028.

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