• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.6
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• pp.564-574
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• 2008

This study investigates a long-term variation of annual maximum surge heights(AMSH) and main characteristics of high surge events, which is influenced by the global warming and intensifying typhoons, using sea level data at Sokcho and Mukho tidal stations over 34 years ($1974{\sim}2007$). It is found that the there is a longterm uptrend of the AMSH at Sokcho (8.3 cm/34yrs) and at Mukho (8.7 cm/34yrs), which is significant within 95% confidence level based on the linear regression. The statistical analysis reveals that 53% of the AMSH occurs during typhoon's event in both tidal stations and the highest surge records are mostly produced by the typhoon. It is concluded that the uptrend in the AMSH is attributed by the increasing typhoon activities globally as well as locally in Korea due to the increased sea surface temperature in tropical oceans. The continuous efforts monitering and predicting the extreme surge events in the future warm environments are required to prevent the growing storm surge damage by the intensified typhoon.

• Korean Security Journal
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• no.61
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• pp.9-38
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• 2019

As the scales & density of the Korean national infrastructures have been increased, they will be identified as rich and attractive potential targets for intensified North Korea's attack in the rear region and terrorism attack. In addition, due to changes in security environment such as drone threats and lack of security forces under the 52-hour workweek law, I think that it is the proper time point to reevaluate the effectiveness and appropriateness of the current physical protection system and its shift to a new system. In this study, the direction and improvement of the perimeter physical protection systems of the national infrastructures are to be studied from the viewpoints of its concepts of operations and design methodology, focusing on the nuclear power plant. The reason why we focus on nuclear power plants is because they cause wide-range and long-term damages caused by radioactive materials disperal and pollution, along with short-term damage caused by the interruption of electricity generation in the event of damage to nuclear power plants. With the aim of extracting improvement directions, as we will comprehensively review domestic research trends and domestic·overseas related laws, and consider Korea's specificity, we try to reframe the concept of operation - systematization, mobilization and flexibility -, and establish criteria on system change. In order to improve the technical performance of the new perimeter physical protection system, we study on high-fidelity·multi-methodology based integrated design methodology, breaking from individual silo-type design methods, and I suggest improvement of government procurement, its expansion to export business and other national infrastructure.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.376-387
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• 2021

This study is to analyze the research trends related to the 'glass ceiling' phenomenon using big data analysis methods and to suggest social implications. To analyze the research trends of 'glass ceiling', the historical event that broke the 'glass ceiling' was set as an important issue, and keywords were collected by dividing park's term into three. Before, throughout and after, her term. As a result of frequency analysis, research was conducted based on 'public servants' which was selected as the main keyword in the first period, while 'women's work family compatibility' was chosen as the main keyword group in the second period. In the third period, keywords for women's occupational groups were being diversified. As a result of applying CONCOR techniques to make the studied main topics grouped, we were able to confirm that the main issues were the differentiating factors, the customary gender discrimination culture, the jobs aimed for studying, the work-family balance, the glass ceiling and the organizational performance adjustment factors, the public sector, organizational performance, and the private sector. Besides work-family compatibility support system, it was suggested as a social implication that research on improving the system to resolve the glass ceiling factor and to expand the target jobs to give solutions to real-life issues were needed, and also suggested that research on the 'glass ceiling' which the general public perceives through social medias or articles in the news, was needed in the future.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.4
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• pp.342-347
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• 2008

Mortality and growth rate of the soft coral, Dendronephthya gigantea, from Jejudo Island on the southern coast of Korea were investigated from February 2003 to October 2004 using SCUBA diving. 48 individuals with variable sizes of D. gigantea of the depth of 15m were tagged with flagging tapes and plastic films, and then monitored with two month intervals. The average mortality of two-month term for the study period was 50.4%, with the peak of 84.6% during the summer storms in August - October 2003. About the size class mortality, individuals of size class I(${\leq}$10 cm) showed the highest mortality, followed by size class III(>20 cm) and size class II(10 cm-20 cm). Growth rate did not show a seasonal pattern. For growth in length, individuals of D. gigantea grew about 3cm in average for two-month period, with a maximum growth of 6.4 cm which occurred in August - October 2003. For growth in diameter, individuals grew about 0.3 cm for 2 month term, with a maximum of 1.4cm in April - June 2004. Individuals of size class I usually grew faster than those of larger size classes. D. gigantea population in Jejudo Island was strongly affected by summer storms, which was due to annual event of summer typhoon. Never the less, it appears that the local population can be maintained by fast growth of the juvenile stage and active recruitment to compensate the high mortality caused by the summer disturbance.

• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.311-321
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• 2009

Long-Term Hydrologic Impact Assessment (L-THIA) was modified to improve runoff and pollutant load prediction for Korean watersheds with changes in land use classification and event mean concentration produced from observed data in Korea. The L-THIA model was linked with SCE-UA, which is one of the global optimization techniques, to automatically calibrate direct runoff. Modified L-THIA model was applied to Gumho River Basins to analyze spatial distribution of nonpoint source pollution. The results of model calibration during 1991~2000 and validation during 1981~1990 for direct runoff represented high model efficiency of 0.76 for calibration and 0.86 for validation. As a results of spatial analysis of nonpoint source pollution, the BOD was mainly loaded from urban area but SS, TN, and TP from agricultural area which is mainly located along the stream. Modified L-THIA model improve its accuracy with minimum imput data and application efforts. From this study, we can find out the L-THIA model is very useful tool to predict direct runoff and pollutant loads from the watershed and spatial analysis of nonpoint source pollution.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.3
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• pp.58-68
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• 1988

Most of the Korean watersheds are mountaineous and consist of various soil types and land uses And seldom watersheds are found to have long term hydrologic records. The SNUA, a hydrologic watershed model was developed to meet the unique characteristics of Korean watershed and simulate the storm hydrographs from a small mountaineous watershed. Also the applicability of the model was tested by comparing the simulated storm hydrographs and the observed from Dochuk watershed, Gwangjugun, Kyunggido The conclusions obtained in this study could be summarized as follows ; 1. The model includes the simulation of interception, evaporation and infiltration for land surface hydrologic cycle on the single storm basis and the flow routing features for both overland and channel systems. 2. Net rainfall is estimated from the continuous computation of water balance at the surface of interception storage accounting for the rainfall intensities and the evaporation losses at each time step. 3. Excess rainfall is calculated by the abstraction of infiltration loss estimated by the Green and Ainpt Model from the net rainfall. 4. A momentum equation in the form of kinematic wave representation is solved by the finite differential method to obtain the runoff rate at the exit of the watershed. 5. The developed SNUA Model is a type of distributed and event model that considers the spatial distribution of the watershed parameters and simulates the hydrograph on a single storm basis. 6. The results of verification test show that the simulated peak flows agree with the observed in the occurence time but have relative enors in the range of 5.4-40.6% in various flow rates and also show that the simulated total runoff have 6.9-32% of relative errors against the observed. 7. To improve the applicability of the model, it was thought that more studies like the application test to the other watersheds of various types or the addition of the other hydrologk components describing subsurface storages are needed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1435-1442
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• 2014

This study presented numerical simulations of smooth-bed flows in the rectangular open-channel using the source code by OpenFOAM. For the analysis of the turbulent flow, Large Eddy Simulations were carried out and the dynamic sub-grid scale model proposed by Germano et al. (1991) is used to model the residual stress term. In order to analyze the coherent structure, the uw quadrant method proposed by Lu and Willmarth (1973) is used and the contribution rate and the fraction time of the instantaneous Reynolds stress are obtained in the Reynolds stress. The results by the present study are analyzed and compared with data from previous laboratory studies and direct numerical simulations. It is found that the contribution rate of the ejection events is larger than that of sweep events over the buffer layer in the open-channel flow over the smooth bed, however, the frequency of the sweep event is higher than that of the ejection events.