Non-Linear Machine Learning with Active Sampling for MOX Drift Compensation
| العنوان: | Non-Linear Machine Learning with Active Sampling for MOX Drift Compensation |
|---|---|
| المؤلفون: | Tamara Matthews, Horacio González-Vélez, Muhammad Iqbal |
| المساهمون: | Muhammad Iqbal |
| المصدر: | BDCAT Conference papers |
| بيانات النشر: | IEEE, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Algebraic interior, Neural Networks, Computer science, 02 engineering and technology, Extreme Gradient Boosting, Machine learning, computer.software_genre, 01 natural sciences, Machine Learning, Support Vector Machines, Longitudinal Data Analysis and Time Series, Sensitivity (control systems), MOX fuel, Artificial neural network, business.industry, Data Science, 010401 analytical chemistry, Detector, Non-Linear Learning Methods, Sampling (statistics), Humidity, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Multivariate Analysis, Artificial intelligence, 0210 nano-technology, business, Numerical Analysis and Scientific Computing, computer, XGBoost |
| الوصف: | —Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI’s HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution. Index Terms—Neural Networks, Extreme Gradient Boosting, XGBoost, Support Vector Machines, Non-Linear Learning Methods, Machine Learning |
| وصف الملف: | application/pdf |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36429e045941d72673a249243557c09bTest https://doi.org/10.1109/bdcat.2018.00016Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....36429e045941d72673a249243557c09b |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |