• The korean journal of orthodontics
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• v.20 no.2
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• pp.293-304
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• 1990

The purpose of this study was to describe the morphologic differences between long-face adults and normal-face adults on the lateral and P-A cephalograms. Long-face and normal-face subifects were selected clinically, and then each of them was taken the lateral cephalogram. According to SN-MP angle and ATFH on the lateral cephalogram, long-face group and normal-face group were classified. 2 long-face adults and 18 normal-face adults were collected, and each of them was taken the P-A cephalogram. The results were as follows: 1. The morphologic differences between long-face adults and normal-face adults were closely related to mandibular morphology. 2. Long-face adults, compared with normal-face adults, demonstrated significant increase in ALFH, and significant decrease in ramus height. 3. Long-face adults, compared with normal-face adults, demonstrated significant increase in AUDH and, ALDH, especially in ALDH. 4. On the P-A cephalogram, no measures of transverse dimension demonstrated significant differences between two groups. 5. On the P-A cephalogram, facial height/facial width ratio was significantly larger than normal in the long-face adults, and in the normal-face adults, facial height/facial width ratio was approximately 90%. 6. In the correlation analysis of SN-MP angle and ATFH with all the other variables, the correlation coefficients of SN-MP angle and PTFH/ATFH that of ATFH and ALFH on the lateral cephalogram demonstrated the highest value, and on the P-A cephalogram, SN-MP angle and Cg-GA-Me (Lt.), ATFH and lower facial height demonstrated the highest value of correlation coefficients.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.177-186
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• 2019

Wind power tower has been used to augment the performance of VAWT (Vertical Axis Wind Turbine). However, inappropriately designed wind power tower could reduce the performance of VAWT. Hence, an optimization study was conducted on a wind power tower. Six design variables were selected, such as the outer radius and the inner radius of the guide wall, the adoption of the splitter, the inner radius of the splitter, the number of the guide wall and the circumferential angle. For the objective function, the periodic averaged torque obtained at the VAWT was selected. In the optimization, Design of Experiment (DOE), Genetic Algorithm (GA), and Artificial Neural Network (ANN) have been applied in order to avoid a localized optimized result. The ANN has been continuously improved after finishing the optimization process at each generation. The performance of the VAWT was improved more than twice when it operated within the optimized wind power tower compared to that obtained at a standalone.