setsci Setsci conference indexing system 2618-6446 SET Technology 2044d735-674b-4ecc-9adb-5efcdd4d995d Non-Magnetic Materials Assignment based on Artificial Neural Network Özkaya Umut Öztürk Şaban Seyfi Levent Akdemir Bayram Selçuk University  Amasya University  23 06 2018 2 410 415 © 2018 SET Technology 2018 SET Technology CC BY 4.0

In this paper, signal recognition system is proposed for classification of soil types and buried object by using feature extraction and artificial neural networks. Using GprMax simulation program, 300 GPR A scan signals were obtained for different ground planes and materials with 206 Hz sampling frequency. Since the materials of buried objects are non-magnetic, these signals contain electric fields in the z direction (Ez) and magnetic fields in the x and y directions (Hx, HY). Features are extracted from them in time and frequency domain such as mean, standard deviation, skewness, kurtosis etc. Therefore, signal features are obtained for inputs of artificial neural networks. Feed forward backpropagation neural network is chosen as an artificial neural network model. The outputs of neural network are types of soil and materials. Also, classification accuracy is examined with changes in learning rates, iteration numbers, momentum constant and number of neurons in hidden layer. The obtained results show high accuracy rate for soil and material recognition.  

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