**5. Experimental results**

the class to which the data belong, is determined on the basis of the majority rule. This process is faster if the binary decision trees are executed in parallel, and the decision quality is higher than if only one binary decision tree is used. As a 1D simulator, or a Virtual Twin, processing time for estimating the target area is

As another AI-based approach, a SNN is used. "Shallow" means it has only one hidden layer, and this is expected to fasten processing. The estimation performance

The deep neural network performs well in image recognition, and it has been used in autonomous driving of automobiles, appearance inspection, image recognition of robots, and other applications. However, it requires a large amount of training data, and accordingly, a huge amount of processing time is needed in network training. In addition, the inference should also be executed on a GPU machine. On the contrary, the size of signal data is not so big because they are timeseries, and no deep neural network may be needed for its recognition. For example, the on-line hammering sound inspection based on the simplest neural network with no hidden layers, a support vector machine, achieves more than 99% accuracy

In the following, an SNN, as shown in **Figure 6**, is constructed. Here, the number of hidden layers is only one, and this layer has ten neurons. The number of neurons in the input layer equals the size of input signal length or statistical features such as the maximum value, minimum value, average value, median value, standard deviation value, peakedness value, and skewness value, all of which are obtained from the input sensor signal. The number of neurons in the output layer is two, that is, areas with or without lawn grasses, or with short or long lawn grasses. A hyperbolic tangent activation function and a softmax function are incorporated

essential.

**4.2 Shallow neural network**

*Robotics Software Design and Engineering*

within a short time [19].

into the output layer.

**Figure 6.**

**128**

*Shallow neural network configuration.*

*4.2.1 Design of shallow neural network*

of SNN is compared with that of the RF algorithm.

This section focuses on the experiments and evaluations of the RF algorithm and SNN on real-world sensing data.
