• Structural Engineering and Mechanics
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• v.50 no.4
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• pp.471-486
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• 2014

The number of explosive attacks on civilian structures has recently increased. Protection of structure subjected to blast load remains quite sophisticated to predict. The use of the pyramid cover system (PCS) to strengthen sandwich structures against a blast terror has great interests from engineering experts in structural retrofitting. The sandwich steel structure performance under the impact of blast wave effect is highlighted. A 3-D numerical model is proposed to study the PCS layer to strengthen sandwich steel structures using finite element analysis (FEA). Hexagonal core sandwich (XCS) steel panels are used to study structural retrofitting using the PCS layer. Field blast test is conducted. The study presents a comparison between the results obtained by both the field blast test and the FEA to validate the accuracy of the 3-D finite element model. The effects are expressed in terms of displacement-time history of the sandwich steel panels and pressure-time history effect on the sandwich steel panels as the explosive wave propagates. The results obtained by the field blast test have a good agreement with those obtained by the numerical model. The PCS layer improves the sandwich steel panel performance under impact of detonating different TNT explosive charges.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.507-520
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• 2014

In this study, the surface displacement was investigated according to the various depth of cover when the tunnel excavation equipment was used in a clay soil. For this the laboratory scaled model test was carried out using the soil sample similar to the in-situ conditions. We carried out four tests according to tunnel depth(1.5D, 2.0D, 2.5D, 3.0D). The distribution of impact due to tunnelling was quantitatively analyzed in the three-dimension by measuring the surface displacement. In addition, the pattern of surface displacements was figured out.

• Journal of the military operations research society of Korea
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• v.36 no.2
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• pp.25-37
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• 2010

This paper deals with achieving the optimal quantity of required PKMs to cover the coastal areas divided into the proper size of sectors, and then using Set Cover Model, Clustered Model, etc. It is optimized via "Requirement Optimization Process" to allocate PKMs reasonably which is considered as conducting mission deployment sectors. This "Hybrid Proper Requirement Model" accommodating the optimization process is introduced and testified by examining a requirement problem.

• Journal of the Korean housing association
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• v.17 no.5
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• pp.47-54
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• 2006

This study aims to evaluate the sound insulation performance of Korean traditional paper(Hanji) windows as a material of environmental friendly building. Six types of traditional windows with 4 types of traditional window positions, were installed in l/2.5 scale model house. And then according to KS F 2235, comparative sound level differences between outdoor and indoor were measured. The main results are as follows; 1) TL(Transmission Loss) of Korean traditional paper windows, which cover one eighth of total balcony window, are ranged from 15 to 19 dB(A) in the living room and from 8 to 11 dB(A) in the balcony space. 2) TL of Korean traditional paper windows, which cover one fourth of total balcony window, are ranged from 10 to 19 dB(A) in the living room and from 8 to 10 dB(A) in the balcony space. 3) TL of Korean traditional windows with one side-one layer paper is ranged from 10 to 21 dB(A) and two side-one layer paper is 15 to 23 dB(A) and two side-two layer paper is 19 to 23 dB(A) respectively.

• Atmosphere
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• v.28 no.2
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• pp.153-162
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• 2018

The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering $El-Ni{\tilde{n}}o$ Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of interannual variability in the troposphere and stratosphere, enhance the seasonal prediction over the Northern Hemispheric surface air temperature, even though their impacts are dependent on the predicted month and region. In particular, the prediction of Korean surface air temperature in midwinter is substantially improved. In December, ENSO improved about 10% of prediction skill compared without it. In January, ENSO and QBO jointly helped to enhance prediction skill up to 36%. These results suggest that wintertime surface air temperature in Korea can be better predicted by considering not only high-latitude surface conditions (i.e., Eurasian snow cover extent and Arctic sea ice concentration) but also equatorial sea surface temperature and stratospheric circulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.215-223
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• 2020

The objective of this study is to develop a prediction model of specific degradation using data mining classification especially for the rivers in South Korea river. A number of critical predictors such as erosion and sediment transport were extracted for the prediction model considering watershed morphometric characteristics, rainfall, land cover, land use, and bed material. The suggested model includes the elevations at the mid relative area of the hypsometric curve of watershed morphomeric characteristics, the urbanization ratio, and the wetland and water ratio of land cover factors as the condition factors. The proposed model describes well the measured specific degradation of the rivers in South Korea. In addition, the development model was compared with the existing models, since the existing models based on different conditions and purposes show low predictability, they have a limit about the application of Korean River. Therefore, this study is focusing on improving the applicability of the existing model

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.882-886
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• 2004

In this section, the proposed parameter selection procedure is applied to two example problems, one is the plate example given in section 2.2 and the other is a cover structure of hard disk drive (HDD).

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.