earticle

영어

Temperature modulation of metal oxide semiconductor (MOS) gas sensors has been widely used due to its higher discriminating power. The temperature modulation alters the kinetics of the gas-sensor interaction leading to characteristic response patterns. However, the selection of frequencies and duty cycles is based on trial and error method. In this paper, we have introduced a method to systematically determine the optimal set of modulation frequencies and duty cycles using system identification theory for sensor modeling. Pulse modulation being a popular method of feature extraction of MOS sensors, optimization of parameters of pulse modulation becomes very significant. In our work, system identification has been applied to select the sensor model that provides the most stable and desired sensor response, hence solving problem of choosing the best frequency and duty cycle of the temperature modulating signal of the MOS sensor. The estimation of model parameters is done using iterative prediction-error minimization (PEM) method. The best suited transfer function was chosen for the MOS gas sensors based on the sensor stability and then the sensors were operated at the respective best frequencies and duty cycles. Principal Component Analysis (PCA) was used to visualize the different sample gas patterns. Data classification was performed using supervised neural network classifiers; namely the Multi-Layer Perceptron (MLP) network and Radial Basis Function (RBF) network and the classification percentage before and after optimization were compared henceforth.