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Fruit Volatile Analysis Using an Electronic Nose
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Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose.

Tao Liu1, Dongqi Li2, Jianjun Chen3

  • 1School of Microelectronics and Communication Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing 400044, China. cquliutao@cqu.edu.cn.

Sensors (Basel, Switzerland)
|November 23, 2018
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Summary

This study introduces AL-ACR, a novel active learning method to address gas sensor drift in electronic noses. AL-ACR effectively manages online drift data, improving E-nose performance and accuracy.

Keywords:
active learningdrift counteractionelectronic noseonline

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Area of Science:

  • Sensor Technology
  • Artificial Intelligence

Background:

  • Gas sensors are crucial for electronic noses (E-noses) but suffer from drift, degrading performance over time.
  • Online calibration for sensor drift is challenging, necessitating new approaches.

Purpose of the Study:

  • To explore an effective online approach for managing gas sensor drift in E-nose systems.
  • To propose and evaluate a novel active learning method, AL-ACR, for dynamic drift counteraction.

Main Methods:

  • Utilized active learning (AL) to provide reliable labels for online instances, overcoming limitations of conventional AL methods.
  • Developed AL on adaptive confidence rule (AL-ACR) to dynamically address online drift data by selecting instances evenly distributed across categories.
  • Evaluated AL-ACR using two E-nose drift databases and compared it against reference methods.

Main Results:

  • AL-ACR demonstrated higher accuracy compared to reference methods on both E-nose drift databases.
  • The study analyzed the impact of labeling number on AL-type methods' performance.
  • Introduced the Labeling Efficiency Index (LEI) to numerically assess labeling contribution.

Conclusions:

  • AL-ACR effectively counters online gas sensor drift in E-nose systems.
  • AL-ACR achieves superior performance with optimal cost-effectiveness among tested AL methods.
  • The proposed method offers a robust solution for maintaining E-nose accuracy in the presence of sensor drift.