• Journal of Soil and Groundwater Environment
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• v.10 no.2
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• pp.28-34
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• 2005

The aim of this article is to assess the application characteristics of the site by remediating oil-contaminated area using DSB (Deep-site Biopile) system. In the contaminated area, the soil was composed of penetrable sand and the leaked oil was spread widely (total 7,201 cubic meters) through 2.5 meter deep underground water flow. DSB system was operated for 30 minutes intervals for 24 hours in a day (30 minutes opεration and 30 minutes stop). To check contamination level change in the contaminated area after DSB system was operated, samples were taken. The result from the site shows that BTEX/TPH contamination level was dropped 50% after 30-day operation of DSB system, and that contamination level was dropped below contamination level check standard after 165 days and the remediation was completed. Unlike traditional biological remediation methods DSB system could efficiently process soil and water which were contaminated by high levels of oil compounds.

• Computers and Concrete
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• v.32 no.6
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Ocean Systems Engineering
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• v.8 no.1
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• pp.21-32
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• 2018

Due to the escalation in hydrocarbon consumption, the offshore industry is now looking for advanced technology to be employed for deep sea exploration. Riser system is an integral part of floating structure used for such oil and gas extraction from deep water offering a system of drill twines and production tubing to spread the exploration well towards the ocean bed. Thus, the marine risers need to be precisely employed. The incorporation of the strengthening material, fiber reinforced polymer (FRP) for deep and ultra-deep water riser has drawn extensive curiosity in offshore engineering as it might offer potential weight savings and improved durability. The design for FRP strengthening involves the local design for critical loads along with the global analysis under all possible nonlinearities and imposed loadings such as platform motion, gravity, buoyancy, wave force, hydrostatic pressure, current etc. for computing and evaluating critical situations. Finite element package, ABAQUS/AQUA is the competent tool to analyze the static and dynamic responses under the offshore hydrodynamic loads. The necessities in design and operating conditions are studied. The study includes describing the methodology, procedure of analysis and the local design of composite riser. The responses and fatigue damage characteristics of the risers are explored for the effects of FRP strengthening. A detail assessment on the technical expansion of strengthening riser has been outlined comprising the inquiry on its behavior. The enquiry exemplifies the strengthening of riser as very potential idea and suitable in marine structures to explore oil and gas in deep sea.

• Journal of Navigation and Port Research
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• v.45 no.2
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• pp.54-60
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• 2021

The aim of this study is to determine the critical factors and construction requirements for a new deep-water seaport system in the Lach Huyen area in northern Vietnam. In this study, the Analytic Hierarchical Process (AHP ) method was used to evaluate the importance of the criteria and subcriteria. The results were as follows: the principal criterion "geographical location (0.151)" ranked as the most important criterion for building a new deep-water port system, which is affected by the subcriteria "direct sea route with mega-vessel" and "good liner connectivity index". The principal criterion "port competency (0.145)" ranked second; thus, it can be concluded that good competitiveness of a port' will provide many benefits to the port and the region. Regarding the implication, the established evaluation framework can be used for port construction to make a more reasonable judgment. In a future study, the scope of evaluation factors should be widened, involving participation of broader stakeholders, such as shipping companies, forwarders, and logistics companies.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.160-163
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• 2001

In order to develop a nodule conveying system through a flexible pipe out of the deep-seabed manganese nodule miner, an experimental study of the solid-water slurry flow in vertical pipe is performed as the first stage of total experiments. Hydraulic characteristics of the pipe slurry flow such as slip velocity, transport concentration and pressure gradient are investigated for the size of particle, load ratio, and flow rate of water. The higher the load ratio is, the larger the transport concentration and pressure gradient become. The bigger the size of particles is, the larger the pressure gradient becomes. The effectiveness of the flow rate to hydraulic performance is also investigated. This results are to be used for designing crusher and pump, and operating the conveying device.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.4
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• pp.227-238
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• 2013

The purpose of this study is to develop a process technology to produce high hardness drinking water which meet drinking water standard, remaining useful minerals like magnesium and calcium in the seawater desalination process while removing the sulfate ions and chloride ions. Seawater have been separated the concentrated seawater and desalted seawater by passing on Reverse Osmosis membrane (RO). Using Nano-filtration membrane (NF), We were prepared primary mineral concentrated water that sodium chloride were not removed. By the operation of electro-dialysis (ED) having ion exchange membrane, we were prepared concentrated mineral water (Mineral enriched desalted water) which the sodium chloride is removed. We have produced the high hardness water to meet the drinking water quality standards by diluting the mineral enriched desalted water with deionized water by RO. Reverse osmosis membranes (RO) can separate dissolved material and freshwater from seawater (deep seawater). The desalination water throughout the second reverse osmosis membrane was completely removed dissolved substances, which dissolved components was removed more than 99.9%, its the hardness concentration was 1 mg/L or less and its chloride concentration was 2.3 mg/L. Since the nano-filtration membrane pore size is $10^{-9}$ m, 50% of magnesium ions and calcium ions can not pass through the nano-filtration membrane, while more than 95% of sodium ions and chloride ions can pass through NF membrane. Nano-filtration membrane could be separated salt components like sodium ion and chloride ions and hardness ingredients like magnesium ions and calcium ions, but their separation was not perfect. Electric dialysis membrane system can be separated single charged ions (like sodium and chloride ions) and double charged ions (like magnesium and calcium ions) depending on its electrical conductivity. Above electrical conductivity 20mS/cm, hardness components (like magnesium and calcium ions) did not removed, on the other hand salt ingredients like sodium and chloride ions was removed continuously. Thus, we were able to concentrate hardness components (like magnesium and calcium ions) using nano-filtration membrane, also could be separated salts ingredients from the hardness concentration water using electrical dialysis membrane system. Finally, we were able to produce a highly concentrated mineral water removed chloride ions, which hardness concentration was 12,600 mg/L and chloride concentration was 2,446 mg/L. By diluting 10 times these high mineral water with secondary RO (Reverse Osmosis) desalination water, we could produce high mineral water suitable for drinking water standards, which chloride concentration was 244 mg/L at the same time hardness concentration 1,260 mg/L. Using the linked process with reverse osmosis (RO)/nano filteration (NF)/electric dialysis (ED), it could be concentrated hardness components like magnesium ions and calcium ions while at the same time removing salt ingredients like chloride ions and sodium ion without heating seawater. Thus, using only membrane as RO, NF and ED without heating seawater, it was possible to produce drinking water containing high hardness suitable for drinking water standard while reducing the energy required to evaporation.

• Ocean Systems Engineering
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• v.8 no.3
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• pp.313-327
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• 2018

Meeting the touchdown point (TDP) target box is one of the challenges during catenary riser installation, especially for deep water or ultra-deep water riser systems. TDP location mismatch compared to the design can result in variation of riser configuration, additional hang-off misalignment, and extra bending loads going into the hang-off porch. A good understanding of the key installation parameters can help to minimize this mismatch, and ensure that the riser global response meets the design criteria. This paper focuses on investigating the potential factors that may affect the touchdown point location, and addressing the challenges both in the design stage and during installation campaign. Conventionally, the vessel offset and current are the most critical factors which may affect the TDP movement during installation. With the offshore exploration going deeper and deeper in the sea (up to 10,000ft), other sources such as the seabed slope and seabed soil stiffness are playing an important role as well. The impacts of potential sources are quantified through case studies for steel catenary riser (SCR) and lazy wave steel catenary riser (LWSCR) in deep water application. Investigations through both theoretical study and numerical validation are carried out. Furthermore, design recommendations are provided during execution phase for the TDP mismatch condition to ensure the integrity of the riser system.