## 4 Empirical Analysis
### Evaluation of Services  
#### 4.3.1 Calculation Process and Analysis Results of All Respondents  
We performed triangular fuzzification of alternatives per evaluation criteria by applying the fuzzy scale per evaluation score in Table 1 to evaluate alternatives based on Fuzzy-TOPSIS. By utilizing Equations (5) and (6), we performed a fuzzification of alternative evaluations consisting of the minimum, average, and maximum values of the TFN per item. Table 7 indicates the results from the 25 valid samples. Using Equations (7) and (8), we calculated the normalized and weighted fuzzy decision matrices. Taking the A-1-1 (Pedestrian detection at night) alternative evaluation of the prevention of life safety evaluation criteria as an example, the upper limit of these criteria (c\({}_{i}^{*}\)) is 4.5. Then, the decision matrix is \(\overline{R}=\left(\frac{0.7}{45},\ \frac{3.1}{45},\ \frac{4.5}{45}\right)\), and the weighted fuzzy decision matrix is calculated as \(\overline{V}=\left(\frac{0.7}{45},\ \frac{3.1}{45},\ \frac{4.5}{45}\right)\times \left(0.28,\ 0.39,\ 0.54\right)=\left(0.04,\ 0.27,\ 0.54\right)\). Using Equations (9) and (10), we calculated FPIS and FNIS, respectively, as shown in Table 8.  
Using Equation (11), we measured the distances of FPIS and FNIS per alternative. Taking FNIS of the A-1-1 (pedestrian detection at night) alternative to the prevention of life safety evaluation criteria as an example, we obtain:  
\[\mathrm{d}(\overline{x},\ \overline{y})=\sqrt{\frac{1}{3}\Big{[}\left(0.04-0.04 \right)^{2}+\left(0.27-0.17\right)^{2}+\left(0.54-0.42\right)^{2}\Big{]}}=0.093.\]  
The final FPIS (\(d_{i}^{*}\)) and FNIS (\(d_{i}^{-}\)) for each alternative calculated by Equation (12) are reported in Tables 9 and 10, respectively. Finally, the proximity coefficient (CC\({}_{i}\)) is calculated by Equation (13), and the ranking of each evaluation alternative is reported in Table 11. CC\({}_{i}\) of Pedestrian detection at night (A-1-1) and nighttime mobility detection (A-1-2) are 0.896, and 0.859, respectively, and ranked first and second. Thus, the importance of preventing traffic accidents that threaten pedestrian safety at night, when CCTV video monitoring is ineffective, was the most highly evaluated. Next, detection service of precursor fire events within buildings (C-2-1, CC\({}_{i}\): 0.854) and facilities (C-2-2, CC\({}_{i}\): 0.810) were also highly evaluated, ranking third and fourth. Clearly, the interest of evaluators and the importance placed on these issues have increased due to the occasional fire accidents during charging electric vehicles, and at construction sites and traditional markets. In particular, in the case of fires in electrical equipment, the prime time is short; however, precursors to fires, such as temperature changes before a fire occurs, can be confirmed through a thermal camera. The relatively high ranking of these factors suggests that these special characteristics of thermal cameras were reflected in the evaluation. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Purpose Compliance**} & \multicolumn{3}{c}{**Service Suitability**} & \multicolumn{3}{c}{**Service Feasibility**} \\ \cline{2-10}
**Classification** & \begin{tabular}{c} **CCTY** \\ **Life Safety** \\ \end{tabular}  & \begin{tabular}{c} **Conformality** \\ **Complementary** \\ **Effects** \\ \end{tabular}  & \ \begin{tabular}{c} **Conformality** \\ **Relevant** \\ **Indices** \\ \end{tabular}