• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.3
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• pp.159-167
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• 2015

In this paper, the errors found in the existed analytical solutions described the mechanism of residual pore-water pressure accumulation were examined and a new analytical was proposed. The new analytical solution was derived by using a Fourier series expansion and separation of variables was verified by comparison with the existed both analytical and numerical solutions and experimental result. The new analytical solution is very simple that there is no need for numerical integration for deep soil thickness. In addition, the solutions of the residual pore-water pressure for finite, deep, and shallow soil thickness reveled that it is possible to approach from finite to shallow soil thickness, but not possible to deep soil thickness because there was discontinues zone between finite and deep soil thickness.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.103-107
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• 2001

The major studies of an ocean circulation of the East/Japan Sea related to evaluate the feasibility and utilization of deep ocean water are reviewed. The major feature of surface current system of the East/Japan Sea is an inflow of the Tsushima Warm Current through the Korea/Tsushima Strait and the outflow through the Tsugaru and Soya Straits. The Tsushima Warm Current has been known to split into two or three branches in the southern region of the East/Japan Sea. In the cold water region of the East/Japan Sea, the North Korean Cold Current turns to the east near 39$^{\circ}$N after meeting the East Korean Warm Current, then flows eastward. The degree of penetration depends on the strength of the positive wind stress curl, according to the ventilation theory. Various current meter moorings indicate strong and oscillatory deep currents in various parts of the basin. According to some numerical experiments, these currents may be induced by pressure-topography or eddy-topography interaction. However, more investigations are needed to explain clearly the presence of these strong bottom currents. This study concludes the importance of topographical coupling, isopycnal outcropping, different wind forcing and the branching of the Tsushima Warm Current on the circulation of the East/Japan Sea.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.1
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• pp.1-17
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• 2013

The investigation for species composition and catch in the Korean deep-water of the East Sea (also known as Sea of Japan) was carried out with trammel nets of 7 mesh sizes (6.1~24.2cm) offshore Donghae (2006) and Yangyang (2007) of Korea. The catches were 1,268kg and composed of 37 species between 200m and 1,200m in depth. The principal species caught were Taknka's snailfish, salmon snailfish, red snow crab, hunchback sculpin, snow crab, spinyhead sculpin, Tanaka's eelpout, Alaska cod and so on. Those were target fish for commercial value except salmon snailfish. The mesh sizes for the largest catch were 10.6cm and 15.2cm in the fishing ground of Donghae and Yangyang, respectively. The habitat of snow crab was shallower than that of red snow crab in both areas. Trammel net enabled to investigate fish in deep-water with small fishing vessel and rather cheap expenses in contrast to bottom trawl that required too much of it. With increasing inner mesh size of trammel net the mean size of some principal species such as Taknka's snailfish, spinyhead sculpin, hunchback sculpin, Pacific cod, snow crab, red snow crab and hybrid between snow crab and red snow crab tended to be large in certain range of mesh size.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.83-88
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• 2003

Recently, deep ocean water (DOW), which is plentiful in the East sea, has been recognized a global resources for 21st century. To develop DOW resource of 300m deep at Gosung sea, the pipeline of about 4 km long is essentially required to establish land based model complex of DOWA techno-park at coastal zone. This study aims to establish design procedure of DOW supplying and utilizing systems, and to complete basic design of every major facilities. To design, various numerical analysis and engineering consideration have been studied by cooperative works for practical use.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.9-16
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• 2018

The development of limited petroleum resources for use with mankind inevitably explores and seeks to develop oil fields in the deep sea area, under the rise of the oil prices market situation. The use of Oceanic Thermal Energy Conversion (OTEC) technology, which operates the power generation facility using the temperature differences between the deep water and the surface water, is progressing actively as a trend to follow. In this study, the application of the Discharged Thermal Energy Conversion (DTEC) was designed and analyzed under the condition that the supply condition of seawater used in the FPSO installed in the deep sea area is changed up to 400m depth. In this case, it was confirmed that the design of the system that can generate more electric power according to the depth of water is confirmed, by thus applying the DTEC system by taking the cooling water at a deeper water depth than the existing design water depth. The FPSO considers the similarity of the OTEC power generation facilities, and will apply the DTEC system to FPSO in the deep sea area to accumulate technology and the conversion to further utilize the OTEC power generation facilities after the end of life cycle of oil production, which could be a solution to two important issues, namely, resource development and sustainable development.

• Journal of Korean Society of Forest Science
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• v.85 no.1
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• pp.66-75
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• 1996

This study was carried out to examine the forest fire effect on water storage characteristics in the forests. Water storage capacity of the burned area was analyzed by several major factors, such as soil pore, maximum water content, effective water storage, and percolation rate. The results obtained from the analysis of major factors are as follows; The deeper soil depth, the less total pore, coarse pore, effective water storage, and percolation rate. However, fine pore increased slightly in both burned area and control plot. As compared with control plot, burned area showed lower percolation rate, coarse pore, and effective water storage, but higher values of fine pore. Directly after forest fire, the soil pore is little affected. But as the time passes, top soil structure changes and soil pore also is affected even in a deep soil. Estimated effective water storage was lower at top soil of Namcheon and at deep soil of Namha in all the burned areas, but slowly decreased in deep soil compared to control plots. Therefore it was concluded that forest water storage capacity was greatly affected by the forest fire.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.263-266
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• 2003

The purpose of this study is to investigate the effects of ponding depth treatment on water balance in paddy fields. The ponding depth treatments were very shallow, shallow and deep. Daily values of rainfall amount, ponding depth, irrigation water, drainage water, evapotranspiration, and infiltration were measured in the field. The medium ponded plots saved irrigation water about 16% to 53.4% without any statistical difference in rice growth and yields. Hence, the medium ponded depth treatment is better than the traditional deep ponded depth to save water in the transplanted rice culture.

• Bulletin of the Society of Naval Architects of Korea
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• v.24 no.1
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• pp.9-16
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• 1987

The second-order steady horizontal force and vertical moment are derived for a freely-floating body in water of finite depth. Momentum relations are used in terms of the Kochin function in the fluid region far from the body. The general results look very similar to those for deep water. The water depth is formally reflected in terms of the ratio between the phase and group velocities of incident waves. Computations are made for a Series 60 hull($C_B=0.6$) and are compared with the corresponding results of deep water. It is shown that the vertical drift moment for slender ships becomes completely free from water depth when the wave-ship length ratio is taken as parameter.