ArticleSensors (Basel, Switzerland)2018
Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Recent Advances and Unaddressed Challenges in Biomimetic Olfactory- and Taste-Based Biosensors: Moving Towards Integrated, AI-Powered, and Market-Ready Sensing Systems.Sensors (Basel, Switzerland) · 2025Review
- Breathomics: A Non-Invasive Approach for the Diagnosis of Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Review
- Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.Microorganisms · 2023Review
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons.Frontiers in neuroscience · 2021Article
- Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model.Sensors (Basel, Switzerland) · 2020Article
- Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models.Sensors (Basel, Switzerland) · 2019Article
- A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction.Frontiers in neuroscience · 2019Article
- Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.