• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1166-1171
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• 2008

Through Korean high speed train development project "G7 Leading Technology Development Project" from 1996 to 2002, HSR-350X has been developed. It can run the maximum operating speed of 350 km/h. Based on this technology, KTX-2 which will be served commercially has been developed till 2007. This paper introduces the aerodynamic analysis of the High-Speed EMU and shows the results of optimized aerodynamic nose shape design techniques and clean pantograph panhead original techniques study. These are the important parts of developments for high speed train which maximum speed is 400 km/h. Especially for decrease of tunnel micro pressure waves, the optimized nose area distributions were derived and the characteristics of micro pressure wave were analyzed. The robust optimized pantograph panhead shapes investigated to improve the performance and decrease the vortex flow which is thought to be its noise source. These shapes are clean and robust to external disturbances like unsteady accelerated flow or side wind was derived. Finally aerodynamic performances was verified with PIV and smog visualization by wind tunnel test.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.358-369
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• 2001

KTX will be operated on Kyun-Pu High Speed Railroad Line around end of 2,003 and KHST(G7 Korea High Speed Train) will be carried out the development running test at Kyun-Pu High Speed Railroad Line from middle of 2002. By the way, the conventional Ho-Nam railroad line was passed the limit capacity of transportation at some area from 1997. For solving of this matter, Ho-Nam railroad line need new high speed railroad line for high transportation capacity of passengers now. This report was studied about the rolling stock system design used new technology of KHST and KTX for Ho-Nam High Speed Railroad.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.389-397
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• 2020

This study is a descriptive research to investigate the effects of nurses', job embeddedness and nursing work environment on turnover intention. The participants were conducted in 4 general hospitals with more than 300 hospital beds located in G metropolitan city and targeted 203 nurses who understood and agreed with the purpose of the study, and conducted from october to december 2019. The data were analyzed using SPSS 23.0 program, frequency analyzed t-test, ANOVA, Pearson's correlation, and Multiple regression. The factors influencing on turnover intention of clinical nurses were found to have negative effects on job embeddedness and nursing work environment. Therefore, it is necessary to develop and train programs to increase the job embeddedness of clinical nurses. At the same time, efforts should be made to improve the nursing work environment to reduce the turnover intention of their.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.60.2-61
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• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• Smart Media Journal
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• v.9 no.2
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.