## 3 Experimental Results
### Experiment Results
#### 3.5.5 Experiment with the improved expert database  
The expert database can be improved by adding an uncertainty probability, as shown in Table 6. The last column of Table 6 is the uncertainty probability, which represents that it is hard to judge whether there is fire according to the current data collected by the sensors. The uncertainty probability  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Probability**} & \multicolumn{2}{c}{**Evidence 3**} \\ \cline{2-3}  & **Fire** & **No Fire** \\ \hline Temperature & 0.1866 & 0.8134 \\ Smoke & 0.8742 & 0.1258 \\ CO & 0.3911 & 0.6089 \\ Flame & 0.0800 & 0.9200 \\ Humidity & 0.1264 & 0.8736 \\ Fusion result & 0.1532 & 0.8468 \\ \hline Real condition & \multicolumn{2}{c}{No fire} \\ \hline \end{tabular}
\end{table}
Table 4: Fire probability data and fusion result in Condition 3.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Probability**} & \multicolumn{2}{c}{**Evidence 1**} \\ \cline{2-3}  & **Fire** & **No Fire** \\ \hline Temperature & 0.5448 & 0.4552 \\ Smoke & 0.5275 & 0.4725 \\ CO & 0.9211 & 0.0789 \\ Flame & 0.1250 & 0.8750 \\ Humidity & 0.3369 & 0.6631 \\ Fusion result & 0.6037 & 0.3963 \\ \hline Real condition & \multicolumn{2}{c}{Fire} \\ \hline \end{tabular}
\end{table}
Table 5: Fire probability data and fusion result under Condition 3.  
decreases when the probability of fire increases, which is more reasonable than Table 1. On the basis of Table 6, the fusion result should also have three terms: the probability of fire, the probability of no fire and the uncertainty probability.  
The alarm framework is defined as \(\Theta=\{Y\), \(N\), \(\ldots\), \(U\}\); \(Y\) denotes that a fire has taken place; \(N\) denotes without fire; and \(U\) denotes uncertainty about whether there is a fire. We used an automatic generator to heat materials for 1 min, then used the five sensors to obtain the data for three periods of time, where each period lasted 30 s. We give some basic probability values for the five sensors, based on specialist knowledge and the basic probability values of all sensors in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **CO** & **Temperature** & **Smoke** & **Flame** & **Humidity** & **Probability of Fire** & **Probability of No Fire** & **Uncertainty Probability** \\ \hline
1 & 0.1 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.20 & 0.65 & 0.10 \\
2 & 0.2 & 0.3 & 0.2 & 0.3 & 0.5 & 0.30 & 0.60 &  
The basic probability values of given sensors are fused with three periods in the time domain by Formula (12), and the results are shown in Table 8.  
Finally, we fuse the accumulation of basic probability values from five sensors with three periods in the space domain via Formula (13), and the results are \(m\) (\(Y\)) = 0.65, \(m\) (\(N\)) = 0.25, \(m\) (\(U\)) = 0.1. According to the decision method based on the basic probability value, when the threshold values are \(\pounds\)\(\pounds  
temperature, light and other major fire parameters and eliminating redundancies and contradictions that exist in multi-sensor information. Therefore, the proposed method can effectively avoid false positives, underreporting and alarm delays. The calculation results show that the proposed algorithm improves fusion results significantly.  
The authors are grateful to the editors and reviewers for their insightful comments and suggestions. This study was supported by the National Natural Science Foundation of China (81271513, 91324201) and the Fundamental Research Funds for the Central Universities (2042014 kf0043).  
The idea for this research work was proposed by [PERSON] and [PERSON], and the paper writing was completed by [PERSON] and [PERSON].  
The authors declare no conflict of interest.