## 4 Sensor Selection and Sensor Parameters  
From this section to section seven as shown in Figure 15, we describe the design steps of our design methodology.  
Figure 15: Demonstration of the design steps in the ADIMSS automated design methodology.  
As outlined in the introduction, a large and increasing number of commercial sensors is available on the market. Considering the high number of selectable sensors for an application, finding a good or even optimal solution is not an easy task. Selection of the best sensor is based on the requirements of the measurement system, e.g., accuracy, speed and size, and cost (see Figure 5).  
On the other hand, the quality of the solutions obtainable with intelligent multi-sensor systems also depends on sensor parameters and sensor positions. These two conditions can be optimized to increase the results of intelligent sensor systems with regards to classification accuracy. In applications of gas sensor systems, the operating temperature of semiconductor gas sensors is an example of sensor parameter. The heating element has to be properly controlled to have high sensitivity as well as selectivity [33,34]. Typical sensor response curves are shown in Figure 16. For selected sensors, an optimum heating curve could be evolved.  
To obtain the best combination of multi-sensor and the optimal sensor parameters, an intrinsic local optimization method is proposed. The local optimization process of intrinsic method is shown in Figure 17, where the sensors are directly connected to the computational model and optimization algorithms. The candidate solution of PSO or GA in this problem represents the indices of selected sensors and their determined sensor parameter settings. The binary representation is used to encode the sensors, where the value of 'one' means that sensor is selected and 'zero' means the opposite. The multi-sensor can be composed of single sensors and sensor arrays. The sensor parameter can be encoded using either integer representation or floating-point representation, depending on the problems. The representation of a candidate solution is described as follows:  
\[\mathbf{x}_{i}=\left\{\left<s_{1},s_{2},\ldots,s_{m}\right>,\left<h_{1},h_{2}, \ldots,h_{m}\right>\right\} \tag{25}\]  
\[INFO=\left\{\left<\mathrm{bin}\right>,\left<\mathrm{int}\right>\right\} \tag{26}\]  
Figure 16: A snapshot of response curves of a gas sensor is shown for H\({}_{2}\) (7 ppm), CH\({}_{4}\) (1,000 ppm), ethanol (2.4 ppm), and CO (400 ppm) at 70% relative humidity.  
where \(s_{i}\)are a bit value of a sensor, \(h_{i}\) are a parameter value of a sensor, where \(i=\) (_1, 2,..., m_). The fitness of each of candidate solution in the iterations is evaluated by the classification rate. The designer may define a standard model of an intelligent system to evaluate each of the candidate solutions created by optimization algorithms (GA or PSO) with regard to the classification rate. Instead of using such a standard model, other assessment functions based on nearest neighbor methods can also be directly employed to evaluate the candidate solutions. The sensor selection and parameter setting requires intrinsic optimization, which is in this particular case a resource consuming method due to physical stimuli presentations and data acquisition.