ArticleScientific reports2023
A method of gas sensor drift compensation based on intrinsic characteristics of response curve.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- A Multisource Hardware Sensing Signal Fusion Network for Robust State Prediction and Anomaly Perception.Sensors (Basel, Switzerland) · 2026Article
- Metal-Organic Frameworks as Room Temperature Chemiresistive Ammonia Gas Sensing Material: A Review.Sensors (Basel, Switzerland) · 2026Review
- Precision Gas Sensing Interface Circuit with Digital Potentiometer-Based Dynamic Gain Control.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Prediction and Compensation of Performance Degradation in Flexible Sensors.Micromachines · 2026Article
- Evaluation of an Electronic Nose Coupled with In Vitro Fecal Fermentation as a Screening Tool for Fecal Odor in Cats.Animals : an open access journal from MDPI · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
Sensor drift, which is an inevitable and challenging problem in gas sensing, seriously affects the detection performance of sensor. In this study, a new sensor drift compensation method, which is based on intrinsic characteristic of sensory response, is proposed. The dataset of gas sensor for two types of gas with a period of 36 months are collected and two features (one is steady-state feature, another is transient feature) are extracted. Their relationship, which is found to be certain for different months and sensors, is explored. Then, drift compensation method is processed based on this relationship, aiming to make the drifted sensor features adjusted to that of month 1, which is considered as having no drift phenomenon. Moreover, small amount of dataset is necessary for model building and it has strong scalability. Finally, SVM is employed for proving the performance of the drift compensation method proposed in this study. The results show the efficacy of 22 month of continuous monitoring, which has been enough for most application scenario, and almost 20% of increasement of correct classification rate of SVM after drift compensation, which indicates the effect of drift compensation method.
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Registered trials
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