No abstract

Generally, the results of imaging the limited view data in the inverse scattering problem are relatively poor, compared to those of imaging the full view data. It is known that solving this problem mathematically is very difficult. Therefore, the main purpose of this study is to solve the inverse scattering problem in the limited view situation for some cases by using artificial intelligence. Thus, we attempted to develop an artificial intelligence suitable for problem-solving for the cases where the number of scatterers was 2 and 3, respectively, based on CNN (Convolutional Neural Networks) and ANN (Artificial Neural Network) models. As a result, when the ReLU function was used as the activation function and ANN consisted of four hidden layers, a learning model with a small mean square error of the output data through the ground truth data and this learning model could be developed. In order to verify the performance and overfitting of the developed learning model, limited view data that were not used for learning were newly created. The mean square error between output data obtained from this and ground truth data was also small, and the data distributions between the two data were similar. In addition, the locations of scatterers by imaging the out data with the subspace migration algorithm could be accurately found. To support this, data related to artificial neural network learning and imaging results using the subspace migration algorithm are attached.

Glass was fabricated using refused coal ore obtained from the Dogye coal mine in Samcheok. We additionally used soda ash and calcium carbonate to make a glass with the chemical composition of soda-lime glass, and we also used white, brown, and green glass cullet to make various kinds of colored glass. Transparent glass was fabricated by melting batch materials including refused coal ore at 1550 o C for 1 hr in an electrical furnace. The light transmittance and color chromaticity were measured by a UV/VIS/NIR spectrometer. Transparent glass with a light transmittance of over 80% was fabricated using normal refused coal ore and white glass cullet. Various kinds of colored glass with a light transmittance of 30-80% were fabricated using refused coal ore and brown or green glass cullet. The light transmittance of the mixed color glass samples, fabricated using normal refused coal ore and brown glass cullet and green glass cullet, indicated 30-47%, a relatively low value, in the condition of a cullet ratio of 20-50%. The characteristics of the color chromaticity of the glass samples were indicated in a chromaticity diagram by x-coordinates, y-coordinates, Y (lightness). The values of x-coordinates and y-coordinates were moved with a regular directional property according to the kind and amount of glass cullet. Therefore, we concluded that refused coal ore can be used for raw materials of color glass products like art glass and glass tile.