Drift Compensation on Massive Online Electronic-Nose Responses

Cao, Jianhua; Liu, Tao; Chen, Jianjun; Yang, Tao; Zhu, Xiuxiu; Wang, Hongjin

doi:10.3390/chemosensors9040078

Open AccessArticle

Drift Compensation on Massive Online Electronic-Nose Responses

by

Jianhua Cao

^1,2,

Tao Liu

^1,2,*

,

Jianjun Chen

^1,2,*,

Tao Yang

^1,2,

Xiuxiu Zhu

^1,2 and

Hongjin Wang

^1,2

¹

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

²

Chongqing Key Laboratory of Bio-Perception & Intelligent Information Processing, Chongqing University, Chongqing 400044, China

^*

Authors to whom correspondence should be addressed.

Chemosensors 2021, 9(4), 78; https://doi.org/10.3390/chemosensors9040078

Submission received: 3 March 2021 / Revised: 7 April 2021 / Accepted: 8 April 2021 / Published: 11 April 2021

(This article belongs to the Collection State-of-the-Art in Chemical Sensors Modelling and Theoretical Statements)

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Abstract

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

Keywords: electronic nose; drift compensation; active learning; noisy label problem; mixed Gaussian model; expected entropy

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MDPI and ACS Style

Cao, J.; Liu, T.; Chen, J.; Yang, T.; Zhu, X.; Wang, H. Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors 2021, 9, 78. https://doi.org/10.3390/chemosensors9040078

AMA Style

Cao J, Liu T, Chen J, Yang T, Zhu X, Wang H. Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors. 2021; 9(4):78. https://doi.org/10.3390/chemosensors9040078

Chicago/Turabian Style

Cao, Jianhua, Tao Liu, Jianjun Chen, Tao Yang, Xiuxiu Zhu, and Hongjin Wang. 2021. "Drift Compensation on Massive Online Electronic-Nose Responses" Chemosensors 9, no. 4: 78. https://doi.org/10.3390/chemosensors9040078

APA Style

Cao, J., Liu, T., Chen, J., Yang, T., Zhu, X., & Wang, H. (2021). Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors, 9(4), 78. https://doi.org/10.3390/chemosensors9040078

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Drift Compensation on Massive Online Electronic-Nose Responses

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