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Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models.

Xia Zhao1, Pengfei Li1, Kaitai Xiao2,3

  • 1Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.

Sensors (Basel, Switzerland)
|September 8, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel supervised learning algorithm for gas sensor drift compensation. The multi-classifier approach improves classification accuracy by integrating drift compensation directly into the process, outperforming conventional methods.

Keywords:
LSTMSVMdrift compensationgas recognitionthe multi-classification ensemble learning model

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Area of Science:

  • Sensor Technology
  • Machine Learning
  • Data Science

Background:

  • Sensor drift significantly impacts the reliability of gas sensors.
  • Traditional drift compensation methods using reference gas offer limited classification accuracy.

Purpose of the Study:

  • To develop an advanced supervised learning algorithm for effective drift compensation in gas sensors.
  • To enhance sensor classification performance by integrating drift compensation into the classification process.

Main Methods:

  • A multi-classifier integration approach was proposed, combining Support Vector Machine (SVM) for few-shot learning and an improved Long Short-Term Memory (LSTM) model.
  • The algorithm incorporates sensor characteristics and drift compensation within the multi-class classifier model.

Main Results:

  • The proposed multi-classifier approach demonstrated superior performance on a three-year metal-oxide gas sensor dataset.
  • Higher classification accuracy was achieved compared to existing methods, particularly in the presence of long-term sensor drift.

Conclusions:

  • The integrated multi-classifier system effectively compensates for sensor drift.
  • This method offers a significant improvement in gas sensor classification accuracy and reliability over time.