The temperature distributions were numerically calculated for the two-dimensional transient conduction heat transfer problem of a square plate. The obtained temperature distributions were converted into colors to create images, and they were provided as learning and test data of CNN. Classification and regression networks were constructed to predict representative wall temperatures through CNN analysis. As results, the classification networks predicted the representative wall temperatures with an accuracy of 99.91% by erroneously predicting only 1 out of 1100 images. The regression networks predicted the representative wall temperatures within errors of C. From this fact, it was confirmed that the deep learning techniques are applicable to the transient conduction heat transfer problems.

To apply CNN to a fluid problem, we need a method to effectively convert the physical quantities of fluid into an image. The performance of CNN was evaluated using the image transformation method using the minimum and maximum values of the pressure distribution data and the image transformation methods using the normal distribution of the pressure distribution data. Through the performance evaluation of the learned CNN, the image transformation methods of Method 4 and Method 5, which applied the normal distribution of representative pressure distribution data, were very effective. In particular, Method 5 includes the initial and final pressure distribution data to include overall pressure distribution data, thereby improving the resolution of the color map to improve classification performance.

The numerical analysis of two-dimensional transient flow around the obstacle with rotated square cross sections was carried out. The obtained velocity distributions for each time step and each rotation angle were imaged to provide data for CNN(convolutional neural network). Both classification and regression neural networks were used for prediction of rotation angle. As results The classification method incorrectly predicted the rotation angle in only 2 of the 470 images. The regression method predicted the rotation angle errors within except 2 out of 470 images. From these facts, it could be concluded that both methods can be sufficiently applicable to the flow analysis.

The flow analysis of two dimensional transient flow over the obstacles with rectangular cross sections was performed. And 190 velocity distributions for each aspect ratio were imaged to provide input data for convolutional neural network learning. The classification and regression methods were used in estimating the aspect ratio from given velocity distributions. As a result the classification method was more exact than the regression method. But both the classification and regression methods gave relatively accurate prediction of the defined aspect ratio judging from the imaged velocity distributions. This confirms that the deep learning technique is applicable to the flow analysis.

Steam tables including superheated, saturated and compressed region were simultaneously modeled using the neural networks. Pressure and temperature were used as two inputs for superheated and compressed region. On the other hand Pressure and dryness fraction were two inputs for saturated region. The outputs were specific volume, specific enthalpy and specific entropy. The neural network model were compared with the linear interpolation model in terms of the percentage relative errors. The criterion of judgement was selected with the percentage relative error of 1%. In conclusion the neural networks showed better results than the interpolation method for all data of superheated and compressed region and specific volume of saturated region, but similar for specific enthalpy and entropy of saturated region.

Simultaneous modelling was carried out using the neural networks with three inputs including a distinguishing variable for the steam table. It covered whole steam tables including the compressed, saturated and superheated region of water. And relative errors of the thermodynamic properties such as specific volume, enthalpy, entropy were compared using the neural networks and the linear interpolation method. As a result of the analysis, The neural networks has proven to be powerful in modeling the steam table because it has slightly better results than the interpolation method.

The predictive control system using model-based predictive control is a very effective way to optimize the present inputs considering the states and future errors of the reference trajectory, but it has a drawback in that a control input matrix must be repeatedly calculated with a long calculation time at every sampling for minimizing future errors in a predictive interval. In this study, we applied the neural network simulating the predictive control method for the trajectory tracking control of the mobile robot to reduce complex control method and computation time which are the disadvantage of predictive control. In addition, the neural network showed excellent performance by the generalization even for a different reference trajectory. Therefore, The controller is designed by modeling the model-based predictive control gains for the reference trajectory using a neural networks. Through the computer simulation, the proposed control method showed better performance than the general predictive control method.

The state variables of saturated and superheated region in the steam table were simultaneously modeled using the neural networks. And the results were compared with quadratic spline interpolation and Lagrange interpolation. Two input data without distinguishing parameter were used in the neural networks. For comparison, quadratic spline interpolation method for superheated region and Lagrange interpolation method for saturated region were applied. The overall results revealed that the neural networks were greatly superior to quadratic interpolation method or Lagrange interpolation method.

The steam table in saturated and superheated region was modeled simultaneously using the neural networks. A variable was introduced to distinguish between the saturation and the superheat. The relative errors were compared with the quadratic spline interpolation method. The relative errors by the neural networks were superior to those by the quadratic spline interpolation method over almost all ranges of temperatures and properties. The overall errors in the saturated region were better than those in the superheated region. From the analysis, it was confirmed that the neural networks could be a very powerful tool for simultaneous modeling of superheated and saturated steam table

The thermodynamic state variables in superheated region of steam table are not wholy obtained by measurements. This means that steam table contains a little error. In this study small error was artificially added to superheated variables and modeled using neural networks. The results were compared with the analysis using quadratic spline interpolation method. By and large the relative errors of variables by neural networks were sufficiently small and similar to or less than those by quadratic spline interpolation method. It was concluded that neural networks could be one good way of modeling for superheated steam table.

For the purpose of developing Korean Herbalogy with the plants of Oleceae which grow wild and is planted in Korea, the these and writings on herbalogy, from literature of successive generations, have been thoroughly investigative and the results obtained were as follows: 1. According to sum of 101 species in Oleaceae family they were classified into Syringa genera 29, Ligustrum genera 24, Fraxinum genera 20, Osmanthus genera 20, Forsythia genera 8, Albeliophyllum genera 8. Thus it was noticed that Syringa genera was the main kind, some 29% in total. 2. There were totaled 19 genera and 101 species in Korea and among them modecinal plants are 6 genera, 28 species, in total but the number of species may be added because of similar plants. 3. According to the oriental name which can be used for medical purpose, the medicinal plants beloning to the Oleaceae fimily were classified as Fructus 3, Folium 5, Radix 4, Flos 3, Cortex 4, Lignum. Thus it was noticed that Folium was the Main kind. 4. According to the number of species of the origin plants about each chinese materia medicals, they were classified into FRUCTUS FORSYTHIAE 6, FRUCTUS LIGUSTRI 3, CORTEX FRXINI 2, FRUCTUS SYRINGA 3, FRUCTUS LIGUSTRUM OBTUSI FOLIO 2, FLOS JASMI NUDIFIORUM 1, FLOS JASMINI SANBAC 1. 5. According to nature and foavour of medicinal plants, they were classified into cold, cool ; 9, balance ; 6, wormth ; 3. Thus it was noticed that cold, cool is the main in nature and flavour of medicinal plants unidentified 3. 6. According to the Properties and Principal Curative action, they were classified into, clearing up heat and toxin 7, medicines for rehulating the flow of gi and allevating pain 3, resolving phlegm and cough, invigorating kidney and liver 2, clearing up heat and moisture, healthiness eleminating bloodstasis and aleviating pain, grgulating the flow of gi and invigorating blood circulation 1 each. 7. Comparing o whole medicinal plants 20 kinds, toxic durgs include minor toxin were 3 kinds, 7% of the whole. Thus toxic durgs were rare. From this result, it was revealed that the plants for medical purpose in Oleaceae was 28 kinds of the whole, in which Folium was mostly abundunt, were distributed (over) the whole country (widly) that it will be used for clinical treatments more easily. For (about) unidentifical drugs, it is considered that many experiments and clinical approaches must be continued.