Abstract
We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian learning paradigm.
| Original language | English |
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| Title of host publication | SPIE : Defense, Security and Sensing |
| Volume | 7697 |
| Publication date | 2010 |
| Publication status | Published - 2010 |
| Event | SPIE Defense, Security and Sensing 2010 - Orlando, United States Duration: 5 Apr 2010 → 9 Apr 2010 |
Conference
| Conference | SPIE Defense, Security and Sensing 2010 |
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| Country/Territory | United States |
| City | Orlando |
| Period | 05/04/2010 → 09/04/2010 |
Keywords
- Principal Component Analysis (PCA)
- Non–negative Matrix Factorization (NMF)
- Polymer Coated Quartz Crystal Microbalance Sensor (QCM)
- Principal Component Regression (PCR)
- aussian Process Regression (GPR)
- Artificial Neural Network (ANN)