From the coefficients of confidence matrix, we can detect any fault sensors among the pH sensor array:D=(d11d12?d1nd21d22?d2n????dn1dn2?dnn),(2)where n is the sensor number.In this study, the average data fusion (ADF), self-adaptive data fusion (SADF) and coefficient of variance data fusion (CVDF) are used for the ruthenium dioxide-based electrochemical sensor array. These data fusion technologies are designed using LabVIEW software, purchased from National Instrument (NI) Co. Ltd. The pre-calculation of mean, standard deviation and variance are from measured data before data fusion and the designed block diagram is as shown in Figure 3:Figure 3.Block diagram of LabVIW for pre-calculation with measured data of the sensor array.The mean (��), standard deviation (��) and variance (��2) parameters are obtained from the LabVIEW block diagram.

The LabVIEW program of Figure 3 is integrated and named ��data statistic block.vi��. The data statistic block.vi program diagram is shown in Figure 4.Figure 4.Data statistic block integrated block diagram of Figure 3 with variance, standard deviation and mean.In this study, we applied three data fusion methods to the measured data from the pH sensor array. The average data fusion (ADF) is the easiest data fusion method; it has the same weighted coefficients for the pH electrode array. We denote that a set of pH data from the ith pH sensor is x = x1, x2, ��,xn. The average of the measured data is typically defined as The average of the pH data of the ith pH sensor is used to calculate it using the following equation [10]:x
Belief function theory has been widely applied in intelligent decision systems [1], which is obviously influential in the representation, measure and combination of uncertainty.

In the multisensor information fusion process, the output of each sensor is assigned the same reliability in the Dempster rule of combination [1]. In fact, each sensor has different capacity, so it is not reasonable to keep reliability constant for each sensor, especially for heterogeneous sensors (such as optical sensors, Entinostat RADAR and infrared sensors). Firstly, evaluating the reliability of sensors accurately and amending output evidence are necessary to improve the robustness of fusion systems and decrease the side effects of sensor output with evidence of low reliability. Secondly, the distinction of the sensors’ reliability is an important factor causing conflicts among evidences. By computing the reliability of each sensor, modifying the corresponding evidence is another important way of dealing with high conflicting evidences’ combination.