Abstract
Signal processing and computation of chemical sensor responses is an important step in recognizing the target gas because a chemical sensor is not usually specific to a particular kind of gas. In this paper we present a chemical-sensing system using the Learning Vector Quantization (LVQ) algorithm to classify dynamic sensor array responses. The system consists of an array of piezoelectric quartz-crystal microbalance (QCM) sensors, each coated with a different organic film. We explain the mechanism of the sensor, providing the dynamic response model whose coefficients are used in recognizing the target gas. These coefficients are estimated iteratively using the Kalman filter algorithm, and are transformed to form a pattern vector, which is immediately classified by LVQ in realtime. Identification tests of 14 vapors are performed using the system installed in a personal computer.
| Original language | English |
|---|---|
| Pages (from-to) | 3036-3041 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 4 |
| Publication status | Published - 1997 Dec 1 |
| Externally published | Yes |
| Event | Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA Duration: 1997 Oct 12 → 1997 Oct 15 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture