ArticleSensors (Basel, Switzerland)2019
Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep Learning-Based Prediction and Compensation of Performance Degradation in Flexible Sensors.Micromachines · 2026Article
- A method of gas sensor drift compensation based on intrinsic characteristics of response curve.Scientific reports · 2023Article
- Cross-Domain Active Learning for Electronic Nose Drift Compensation.Micromachines · 2022Article
- Degradation Detection in a Redundant Sensor Architecture.Sensors (Basel, Switzerland) · 2022Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.
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Registered trials
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