In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

In this study, the effects of yacon (Smallanthus sonchifolius) leaf on the quality characteristics of cookies were examined. In order to investigate its effects, four different amounts (0%: Control, Y-0.5, Y-1.0, Y-1.5) of yacon leaf powder were added to the cookie dough. Among the physicochemical and sensory characteristics, the density and pH of the dough, spread factor, color value, firmness, consumer acceptability, and Pearson's correlation coefficients were measured. Although there was no significant difference in pH of the doughs among the groups, density significantly decreased with increasing amount of yacon leaf powder (p<0.05). While the L, a, b values, and hardness decreased significantly, DPPH free radical scavenging activity increased significantly as the content of yacon leaf powder increased (p<0.05). Overall acceptability, appearance, taste, and texture between the control and Y-0.5 groups showed no significant differences. This study suggests the possibility of yacon leaf as an ingredient that increases the functionality of cookies.