## 3 Experiment
Therefore, it is essential to develop techniques that can accurately distinguish between UV radiation emitted from flames and other sources. Figure 4: Experimental results for short-range detection: (**a**) 1.5 m; (**b**) 3 m; (**c**) 5 m; (**d**) 10 m.  
Figure 5: _Cont._  
One approach to minimizing false alarms is to exploit the chaotic nature of radiation from an uncontrolled flame. Unlike other sources such as UV lamps, heaters, and sunlight, combustion involves nonlinear instabilities that result in chaotic emission. This characteristic can be used to distinguish flame signals from other signals that tend to be regular. This study used the Naive Bayes classification algorithm with the signal spectral characteristics in the frequency domain to distinguish between signals from a flame and a controlled UV source (UVB lamp). The proposed approach was evaluated using a dataset obtained from a fire detection system equipped with a UV detector, and the results, presented in Figure 6, demonstrate the algorithm's robust accuracy and efficiency. These findings provide strong evidence for the potential of this technique in effectively reducing false alarms in fire detection systems. Figure 5: Diagram for experimental setup for fire detection at various distances (**a**); real look of experimental setup (**b**); illustration of output signal (**c**). Figure 6: Naive Bayes classifier model applied to the UVtron fire detection system. Distributions of two classes are represented by black and red line. The study aimed to distinguish signals from a flame and a controlled UV source using Fourier spectrum analysis as the classification algorithm. The UVB lamp was modified by limiting the surface area of the radiation source with a pinhole of 10 mm \(\times\) 10 mm in front of it. The resulting signal data obtained from the UVtron sensor appeared as a time series consisting of pulses lasting 10 ms, as shown in Figure 7. The sensor's output signal in response to the UV source consists of a series of fixed-width (10 ms) pulses, exhibiting varying amplitudes (between 4 VDC and 5 VDC) due to conditioning circuit operations. The pulse signals depicted in Figures 6 and 7 represent random selections from the overall output signal obtained during the measurements. The signal pulses from the flame were found to be more discrete than those from the UVB lamp, indicating that the sensor responds to the flame in a more chaotic manner. Fourier spectrum analysis was applied to obtain information on the signal's amplitude at different frequencies, where larger amplitude at any frequency indicated more signal pulses at that frequency and vice versa. Therefore, the Fourier spectrum analysis result for the signal output over a specific period of time was used as the data feature for classification in this study. It is important to note that in the case of the flame, the pulse duration steps for each distance were not uniform at different moments in time, and the time-step magnitude distribution was also non-uniform. Thus, Fourier spectrum analysis was chosen as a robust algorithm that could handle such non-uniformity in the pulse duration and magnitude distribution. The signal pulses from the flame (Figure 7) were found to be more discrete than those from the UVB lamp, indicating that the sensor responds to the flame in a more chaotic manner. Fourier spectrum analysis was applied to obtain information on the signal's amplitude at different frequencies, where a larger the amplitude at any frequency indicating more signal pulses at that frequency, and vice versa. The data feature for classification was therefore the Fourier spectrum analysis result for the signal output over a specific period of time. Figure 7: Output signals of the sensor responding to the UV lamp placed at different distances. (a) 1.5 m; (b) 3 m; (c) 5 m; (d) 10 m.  
To extract the features, the time series of the output signal was divided into windows of duration 250 ms, corresponding to N = 100 samples each, and denoted as \(\mathbf{x}=[x_{1},x_{2},\ldots,x_{N}]\).