## 5 Result
### Feature Extraction Experiment  
After the cutting process of data processing, the problem of data sample data alignment is solved, and the dimension of the data is reduced to a certain extent. However, the data sample dimension is still too large, and the information is redundant, which will greatly increase the calculation in the training and testing process [30]. Directly reducing the dimension of data samples through random feature matrix mapping is likely to lose a lot of critical information. This study adopts a very good simple, and effective feature extraction method. Table 5 shows all the features extracted by this feature extraction method:  
First, select the steady-state feature, the difference between the maximum data value and the baseline. The selection method is as shown in Equation (29):  
\[\Delta\mathrm{X}=\underset{\mathrm{t}}{\mathrm{max}}\mathrm{x}(\mathrm{t})- \underset{\mathrm{t}}{\mathrm{min}}\mathrm{x}(\mathrm{t}) \tag{29}\]  
However, due to sensor drift, the maximum response and baseline of the sensor may change, so it is not enough to extract a single maximum difference. The effect of sensor drift can be reduced by comparing the differential value with the baseline so that the normalized steady-state characteristic can be expressed as Equation (30):  
\[\Delta\mathrm{X}^{*}=\frac{\underset{\mathrm{t}}{\mathrm{max}}\mathrm{x}( \mathrm{t})-\underset{\mathrm{t}}{\mathrm{min}}\mathrm{x}(\mathrm{t})}{ \underset{\mathrm{t}}{\mathrm{min}}\mathrm{x}(\mathrm{t})} \tag{30}\]  
where \(\mathrm{x}(\mathrm{t})\) represents the time-varying data collected by the sensor, and \(\mathrm{t}\) represents the time \(\mathrm{t}\in[0,\mathrm{T}]\).  
The above two characteristics belong to the steady state characteristics and can only reflect the characteristics of the steady state. During the gas reaction process and desorption process, important features of the data are often preserved. In order to represent the dynamic characteristics of the sensor, a method in the field of econometrics called Exponential Moving Average (EMA) will be used. The calculation method is shown in Equation (31):  
\[\mathrm{y}(\mathrm{t})=(1-\theta)\mathrm{y}(\mathrm{t})+\theta(\mathrm{x}( \mathrm{t})-\mathrm{x}(\mathrm{t}-1)) \tag{31}\]  
In Equation (31), \(\mathrm{t}=1,2,3,\cdots\mathrm{T}\), the initial value \(\mathrm{y}(0)=0\), \(\theta\) is the smoothing parameter, and its values are set to 0.01, 0.001, 0.0001 respectively. Figure 3 shows the exponential moving average transformed graph of the chemical sensor TGS2612 5.65V at 250 ppm methane gas, using different \(\theta\) parameters:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steady State Characteristics** & **Dynamic Features** & \\ \hline \hline  & Adsorption stage & Desorption stage \\ \(\Delta\mathrm{X}\) & \(\mathrm{max}(\mathrm{Y}_{0,01})\) & \(\mathrm{min}(\mathrm{Y}_{0,01})\) \\ \(\Delta\mathrm{X}^{*}\) & \(\mathrm{max}(\mathrm{Y}_{0,001})\) & \(\mathrm{min}(\mathrm{Y}_{0,001})\) \\  & \(\mathrm{max}(\mathrm{Y}_{0,0001})\) & \(\mathrm{min}(\mathrm{Y}_{0,0001})\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Features extracted from the data curve.  
As seen in Figure 3, the graph reflects the dynamic characteristics of the sensor response curve well. The minimum and maximum values of the curve represent the maximum rate of change of the sensor resistance during the adsorption and desorption phases of the sensor array, respectively. At the same time, smoothing parameters are used to reduce the influence of resistance value fluctuations during the sensor response process. A total of three smoothing parameters were selected in this experiment to extract the maximum and minimum values of the data after exponential transformation. Therefore, each sensor has eight features, including two dynamic features and six steady-state features.