*5.2.4 Features for classifying data*

The input data features are (i) maximum value, (ii) minimum value, (iii) average value, (iv) median value, (v) standard deviation value, (vi) peakedness value, and (vii) skewness value. In signal recognition based on machine learning, some



**Figure 10.** *Fusion of sensors for SNN.* features are typically extracted from input signal data during pre-processing. The obtained features are used as input data to a machine learning algorithm. Therefore, for each sensor data, seven neurons are needed in the input layer of SNN shown in **Figure 6**. In the experiments, these features are obtained from a time frame obtained every 3.2 s over 2.3-h measurement data.

The details of collected data are shown in **Table 6**. In the experiments, a subset of time frames from each field data is used for training the SNN, and the obtained model is tested on the remaining test data. The number of time frames used for testing is approximately 670 in each group. The remaining time frames are used for training, as shown in **Table 7**.
