• Journal of the Korean GEO-environmental Society
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• v.19 no.10
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• pp.5-11
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• 2018

The PBD (Plastic Board Drain) method is one of effective ground improvement methods on the soft dredging reclamation ground. This method has outstanding economic efficiency and constructability, and it is widely used for the soft ground improvement. However, the PBD method reduces permeability and drainage capacity of the ground due to the long construction period. Therefore, the nano coated Plastic drain board (PDB) was developed to solve problems. It is the non-metallic electrode and improves the weakness of the PBD method by using electric force of the electro-osmosis method. Various researches have been conducted to apply the nano coated PDB, but these researches were limited to model tests in laboratory. In this study, model and field tests were conducted to assess field applicability of the nano coated PDB. The result showed that the nano coated PDB had the better effect on the ground improvement compared to the normal PDB.

• Journal of Korean Society of societal Security
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• v.1 no.4
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• pp.65-71
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• 2008

This paper presents the characteristics of internal erosion of embankment slopes due to the localized heavy rain. In this study, the existing analysis methods of filter performance in embankment materials were reviewed. Based on the theoretical concept of filter conditions to prevent particles from being carried in from the adjacent embankment materials, new analysis method was suggested. According to the new analysis method for filter performance, experimental programs were carried out to investigate the filter performance for controlling and sealing any leak which develops through the embankment materials as a result of internal erosion. Three sets of small scale laboratory tests were carried out with changing the main influence factors such as rainfall intensity, gradient of slope, embankment material condition. It was found that the new analysis method for filter performance to prevent particles from being carried in from the adjacent embankment materials was more capable approach to design the filter materials in embankment slopes. The new criterion or method for satisfactory filter performance, therefore, was recommended.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.2
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• pp.3-10
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• 2000

The compression wave produced when a high-speed train enters a tunnel propagates along the tunnel ahead of the train. The micro pressure wave related to the compression wave is a special physics phenomena created by high-speed train-tunnel interfaces. A among methods for the purpose of reducing the micro pressure wave is to delay the gradient of the compression wave by using aerodynamic structures. The objective of this paper is to determine the optimum slanted portal using the moving model rig. According to the results, the maximum value of micro pressure wave is reduced by 19.2% for the $45^{\circ}$ slanted portal installed at the entrance of the tunnel and reduced by 41.9% for the $45^{\circ}$ slanted portals at the entrance and exit of the tunnel. Also it is reduced by 34.6% for the $30^{\circ}$ slanted portals installed at the entrance and exit of the tunnel.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.45-53
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• 2012

Hollows are easily made and bearing capacity is lowered near underground structures of concrete pavement because of poor compaction and long term settlement of the ground. Distresses occur and lifespan is shortened because of larger stress induced by external loadings expected than that in the design. In this paper, the distresses of the concrete pavement slab over box culverts were investigated at the Korea Expressway Corporation(KEC) test road. The transverse cracking of the slabs over the culverts was compared between up and down lines with different soil cover depth. The box culvert without soil cover and concrete pavement were modeled and analyzed by the finite element method(FEM) to verify the transverse cracking at the test road. Wheel loading was applied after self weight of the pavement and temperature gradient of the concrete slab at Yeojoo, Gyeonggi where the test road is located were considered. Positions of maximum tensile stress and corresponding positions of the wheel loading were found for each loading combination. Joint position minimizing the maximum tensile stress was found and optimal slab length over the culverts with diverse size were suggested.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.5
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• pp.419-428
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• 2014

This paper presents a numerical simulation method for the flow around advancing ships in regular waves by using a rectilinear grid system. Because the grid lines do not consist with body surface in the rectilinear grid system, the body geometries are defined by the interaction points of those grid lines and the body surface. For the satisfaction of body boundary conditions, no-slip and divergence free conditions are imposed on the body surface and body boundary cells, respectively. Meanwhile, free surface is defined with the modified marker density method. The pressure on the free surface is determined to make the pressure gradient terms of the governing equations continuous, and the velocity around the free surface is calculated with the pressure on the free surface. To validate the present numerical method, a vortex induced vibration (VIV) phenomenon and flows around an advancing Wigley III ship model in various regular waves are simulated, and the results are compared with existing and corresponding research data. Also, to check the applicability to practical ship model, flows around KRISO Container Ship (KCS) model advancing in calm water are numerically simulated. On the simulations, the trim and the sinkage are set free to compare the running attitude with some other experimental data. Moreover, flows around the KCS model in regular waves are also simulated.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.5
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• pp.371-379
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• 2016

In the ship design process, ship motion and propulsion performance in sea waves became very important issues. Especially, prediction of ship propulsion performance during real operation is an important challenge to ship owners for economic operation in terms of fuel consumption and route-time evaluation. Therefore, it should be considered in the early design stages of the ship. It is thought that the averaged value and fluctuation of effective inflow velocity to the propeller have a great effect on the propulsion performance in waves. However, even for the nominal velocity distribution, very few results have been presented due to some technical difficulties in experiments. In this study, flow measurements near the propeller plane using a stereo PIV system were performed. Phase-averaged flow fields on the propeller plane of a KVLCC2 model ship in waves were measured in the towing tank by using the stereo PIV system and a phase synchronizer with heave motion. The experiment was carried out at fully loaded condition with making surge, heave and pitch motions free at a forward speed corresponding to Fr=0.142 (Re=2.55×106) in various head waves and calm water condition. The phase averaged nominal velocity fields obtained from the measurements are discussed with respect to effects of wave orbital velocity and ship motion. The low velocity region is affected by pressure gradient and ship motion.

• 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.

• The Korean Journal of Legal Medicine
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• v.41 no.2
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• pp.32-40
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• 2017

In addition to identifying genetic differences between target populations, it is also important to determine the impact of genetic differences with regard to the respective target populations. In recent years, there has been an increasing number of cases where this approach is needed, and thus various statistical methods must be considered. In this study, genetic data from populations of Southeast and Southwest Asia were collected, and several statistical approaches were evaluated on the Y-chromosome short tandem repeat data. In order to develop a more accurate and practical classification model, we applied gradient boosting and ensemble techniques. To infer between the Southeast and Southwest Asian populations, the overall performance of the classification models was better than that of the decision trees and regression models used in the past. In conclusion, this study suggests that additional statistical approaches, such as data mining techniques, could provide more useful interpretations for forensic analyses. These trials are expected to be the basis for further studies extending from target regions to the entire continent of Asia as well as the use of additional genes such as mitochondrial genes.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.