• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1013-1024
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• 2022

In addition to the assessment measure of electric quality levels, load loss are also a factor in hindering the financial profits of electrical sales companies. Therefore, accurate analysis of load losses generated from distributed power networks is very important. The accurate calculation of load losses in the distribution line has been carried out for a long time in many research institutes as well as power utilities around the world. But it is increasingly difficult to calculate the exact amount of loss due to the increase in the congestion of distribution power network due to the linkage of distributed energy resources(DER). In this paper, we develop smart grid big data infrastructure in order to accurately analyze the load loss of the distribution power network due to the connection of DERs. Through the preprocess of data selected from the smart grid big data, we develop a load loss analysis model that eliminated 'veracity' which is one of the characteristics of smart grid big data. Our analysis results can be used for facility investment plans or network operation plans to maintain stable supply reliability and power quality.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.61-67
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• 2021

In this study, the adequacy of water supply critical factors of urban design was examined. The supply of water supply is analyzed in various ways as the design progresses. Starting with basic data collection and analysis a supply and demand plan is established to calculate the amount of water supply and in this study the adequacy of population estimation and original unit calculation was evaluated. Among the second new cities where actual data can be secured Wirye New Town was selected as the study target area. Related data were analyzed to confirm the future population and the original unit and compared with the measured data. As of September 2020, the population of Wirye New Town was 93,977, showing the appropriateness of about 84% with a planned population of 110,990 confirming that the planned population and the actual population were almost similar. In the case of the original unit, it was calculated as 314 liters per person in Seoul and 320 liters per person in Seongnam at the time of design. As a result, it was found that there was some agreement in the population estimation while examining the supply in the planned city. In the case of Korea, there is a lot of interest in revitalizing the existing city, away from continuous development. Therefore it is judged that there is a need for further research on the adequacy of supply for the old city center.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.120-128
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• 2022

There are 17,000 reservoir dams in Korea, of which more than 85% were built over 50 years ago. Old embankment dams are weakened by internal erosion and suffusion phenomena due to preferential leakage paths and this ongoing weakening can cause their failure. Therefore, early warning associated with leakage in an embankment dam is crucial to prevent its failure. An electrical resistivity survey is a non-destructive, real-time and in-situ technique for detecting the development of leakage zones and general conditions of embankment dams. Because of its advantages, the electrical resistivity survey is widely used for reservoir safety inspections. However, the electrical resistivity survey is still not officially included in the precise safety inspection of reservoir dams because it cannot present a quantitative index of dam safety. In this study, we propose a method for calculating the leak index according to the water content evaluated from the electrical resistivity survey and/or induced polarization survey. Particularly, by proposing a quantitative leak index calculation method from monitoring surveys and independent surveys, we provide a theoretical basis for including electrical resistivity and induced polarization surveys as components of the precise safety inspection of reservoirs dams.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• Journal of Environmental Impact Assessment
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• v.32 no.4
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• pp.203-213
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• 2023

The AERMOD model was the most used, accounting for 89.0%, based on the analysis of the environmental impact assessment reports published in the Environmental Impact Assessment Information Support System (EIASS) between 2021 and 2022. The mismatch of versions between AERMET and AERMOD was found to be 25.3%. There was the operational time discrepancy of 50.6% from industrial complexes, urban development projects between used in the model and applied in estimating pollutant emissions. The results of applying various versions of the AERMET and AERMOD models to both area sources and point sources in both simple and complex terrain in the Gunsan area showed similar values after AERMOD version 12 (15181). Emissions are assessed as 24-hour operation, and the predicted concentration in both simple and complex terrain when using the variable emission coefficient option that applies an 8-hour daytime operation in the model is lowered by 37.42% ~ 74.27% for area sources and by 32.06% ~ 54.45% for point sources. Therefore, to prevent the error in using the variable emission coefficient, it is required to clearly present the emission calculation process and provide a detailed explanation of the composition of modeling input data in the environmental impact assessment reports. Also, thorough reviews by special institutions are essential.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.39-51
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• 2019

This study was tested on Ratafia Champagne Trouillard 1947 of old fortified wine and analyzed differences from existing wines. Old fortified wine in Champagne, France and blanding is Pinot Noir, Chardonnay and Pinot Meunier. Alcohol level is 18% and test date is Feb. 15-21, 2019(six days of decanting period/15 p.m. on the last tasting day). Tester is composed of one FICB grand commander one KOV Finland commander. The wine opening was tested for two blades after wire removal and the decanting time was applied to the calculation formula of 2019(this year)-1947(vintage year)/12=6 days set in this study. Aroma smelled like cherries, fruits, soy sauce and licorice and bouquet was identified in five stages. The first stage was presented with the smell of pot, the second stage was light coffee, the third stage smell of fruit and flowers, the fourth stage smell of wild honey and the fifth stage smell of refined brandy. Then, the test was analyzed in seven stages. This study has the following implications: First, the new concept of old wine was applied to fortified wine. Specific computational formulas for the decanting period were derived. The decanting presented five steps of aromas and bouquet. Wine testing has been expanded from the previous five to seven levels. A new taste of Champagne old fortified wine was analyzed.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.27-41
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• 2019

The study tested the old wines of Château Latour 1953 and tried to analyze the differences from the old wines. Even if not the great vintage, the quality change of old wine gives a new flavor, so it requires analysis results from empirical concepts, decanting, and testing. Based on the analysis results, the government wanted to re-evaluate the old wine and give consumers joy and implications for the wine. The wine to be studied is Château Latour 1953 and is an old wine from the French province of Pauillac. Wine blending is known to be 75% of cabernet sauvignon, 20% of melot, 4% of cabernet franc and 1% of petit verdot. The alcohol level is 13% and the test date is July 2-7, 2018(decanting period 5.4 days/15:00 p.m. on July 7). The testing site was a wine cafe in Daegu City, and the tester consisted of one FICB Korean grand commander and one KOV Finland commander and selected Japchae of Korean food as a mariage. The ullage of Chateau Latour 1953 was 3.0cm and was set up for one month for testing. Decanting time was applied to the calculation formula 2018(current year)-1953(vintage year)/12=5.4 days, which was investigated in this study. Aroma smelled of cork, old grapes, tobacco, leaves and leather, the bouquet was identified in five stages, and the testing was analyzed in seven stages.

• Journal of the Korean Geotechnical Society
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• v.23 no.6
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• pp.103-114
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• 2007

Sands distributed in Jeju island's coastal areas, Korea, can be classified as silicate sand derived from volcanic rock, carbonate sand derived from shells, and mixed sands containing both silicate and carbonate sands. These three types of sands typically exist in Jeju coastal areas. Samples of silicate, carbonate and mixed sands were obtained from Samyang beach, Gimnyeong beach, and Jeju harbor area, respectively. Compression tests were conducted to assess the compression characteristics of these sands. As a result of these tests, each sand showed different behaviors. For Samyang beach sand, it appeared that initial compression is a larger than the other two sands. For Cimnyeong and Jeju harbor sands, however, the additional compression occurred after initial compression. This could result from the crushing, shattering, and rearrangement of sand particles. In addition, settlement behavior of Jeju harbor ground according to the construction stages was analyzed using the measured data. It showed that in addition to the initial elastic compression, a considerable additional compression occurred with time. The settlements of Jeju harbor ground were predicted by using the elastic settlement calculation methods (empirical methods) and the compression test method. The empirical methods, which did not consider the crushing, shattering, and rearrangement of particles could show smaller result than that occurring actually.