• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.57-62
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• 2006

Busan New Port, under construction aiming for the hub of Northeast Asia and Partly in operation, had damaged up to 48 billion Won due to Typhoon 'maemi' in 2003. The present criteria of domestic harbor design only describes about the critical wave height with respect to the size of vessel for harbor tranquility. The berth operation ratio which represents the annual available berthing days is depending on the efficiency of cargo handling work and this depends on the motion of the moored vessel due to the wave action and the characteristics of cargo gears. The motion of moored vessel might be related not only to the wave height but also to wave period. Furthermore, the berth operation ratio relies on external forces such as currents and winds, including the characteristics of mooring system and the specification of the moored vessel. In this study we only deal with berth operation ratio in normal sea state, considering wave and current by measured data and numerical calculation. Especially we tried to evaluate the berth operation ratio for each berth adopting the variation of dredging and reclamation plan and the change of wave environment during the process of the new port construction. For better understanding and analysis of wave transformation process, we applied the steady state spectral wave model and extended mild-slope wave model to the related site. This study summarizes comparisons of harbor responses predicted by two numerical predictions obtained at Busan New port site. Field and numerical model analysis was conducted for the original port plan and the final corrected plan.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.4
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• pp.18-29
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• 2013

A new precast concrete (PC) beam and column connection system using non-prestressed wire strands was recently developed. The system is composed of one unit of two-storied PC-column and PC-beams with U-shaped ends. The connection part of the column and beams is reinforced by deformed bars and non-prestressed wire strands in combination for the improvement of workability. Structural performance of this system was verified by several experimental studies. The purpose of this study is developing a design concept of the beam reinforced by deformed bars and non-prestressed wire strands in combination, in terms of the cross-sectional analysis, based on the preceded experiment. A minimum and maximum reinforcement ratio and the calculation formula for the strength of flexural member reinforced by reinforcements having different yield strengths are derived based on KBC2009. Under consideration existing research results for the application of high strength reinforcement bars, the design yield strength of the non-prestressed wire strand is suggested. An example for the cross section design, satisfying the serviceability requirements, demonstrates the applicability of the design concept developed in the study.

• Journal of the Korean Geotechnical Society
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• v.37 no.10
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• pp.13-25
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• 2021

The most widely used method for determining the blast effects of explosives is the Trauzl test. This test is used to measure the explosive power (strength) of a substance by determining volume increase, which is produced by the detonation of a tested explosive charge in the cavity of a lead block with defined quality and size. In this paper, Trauzl lead block test and High speed 3D-DIC (Digital Image Correlation) system were conducted to evaluate the stemming effect of the blast hole. The effects of stemming materials can be expressed as the expansion of the cavity in a standard lead block through explosion of the explosives. The blasting experiment was conducted with emulsion explosives. The stemming material in the blast hole of lead block, which was adopted in this study, were using sand and stone chips. Results of blasting experiment and numerical analysis showed that the expansion rates of lead block were most affected by stone chips followed by sand. Also, as result of dynamic strain measurement on the lead block surface of High speed 3D-DIC system, the displacement and surface strain on the block were the highest in the experiment case of stone chips stemming.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.175-181
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• 2019

In the case of a conventional single stage decompression regulator used for large depressurization in the hydrogen fuel cell system of a fuel cell electric vehicle (FCEV), problems can arise, such as pulsation, slow response, hydrogen brittleness, leakage, high weight, and high cost due to high decompression. Most of these problems can be overcome easily using two decompression mechanisms (two-stage structures). In addition, a wide outlet-pressure control range can be secured if an electronic solenoid is applied to the second decompression. Accordingly, it is necessary to improve the precision of the outlet pressure of a two-stage pressure-reducing regulator and develop techniques, such as leakage prevention, durability, light weight, and price reduction. Therefore, to improve the outlet pressure accuracy and prevent leakage, the structural part before and after decompression to improve the air tightness were divided and the analysis was carried out assuming that the valve part was closed (open ratio: 0%) after each initial internal pressure application.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.109-117
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• 2022

There has been an increasing number of studies on photovoltaic energy generation system in an offshore site with the largest energy generation efficiency, as increasing the researches and developments of renewable energies for use of offshore space and resources to replace existing fossil fuels and resolve environmental challenges. For installation and operation of floating photovoltaic systems in an offshore site with harsher environmental conditions, a stiffness of structural members comprising the total system must be reinforced to inland water spaces as dams, reservoirs etc., which have relatively weak condition. However, there are various limitations for the reinforcement of structural stiffness of the system, including producible size, total mass of the system, economic efficiency, etc. Thus, in this study, a floating breakwater is considered for reducing wave loads on the system and minimizing the reinforcement of the structural members. Wave reduction performances of floating breakwaters are evaluated, considering size and distance to the system. The wave loads on the system are evaluated using the higher-order boundary element method (HOBEM), considering the multi-body effect of buoys. Stresses on structural members are assessed by coupled analyses using the finite element method (FEM), considering the wave loads and hydrodynamic characteristics. As the maximum stresses on each of the cases are reviewed and compared, the effect of floating breakwater for floating photovoltaic system is checked, and it is confirmed that the size of breakwater has a significant effect on structural responses of the system.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Geophysics and Geophysical Exploration
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• v.14 no.1
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• pp.80-87
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• 2011

Autonomous underwater vehicles (AUVs) present the important advantage of being able to approach the seafloor more closely than surface vessel surveys can. To collect bathymetric data, bottom material information, and sub-surface images, multibeam echosounder, sidescan sonar (SSS) and subbottom profiler (SBP) equipment mounted on an AUV are powerful tools. The 3000m class AUV URASHIMA was developed by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). After finishing the engineering development and examination phase of a fuel-cell system used for the vehicle's power supply system, a renovated lithium-ion battery power system was installed in URASHIMA. The AUV was redeployed from its prior engineering tasks to scientific use. Various scientific instruments were loaded on the vehicle, and experimental dives for science-oriented missions conducted from 2006. During the experimental cruise of 2007, high-resolution acoustic images were obtained by SSS and SBP on the URASHIMA around the northern Kumano Basin off Japan's Kii Peninsula. The map of backscatter intensity data revealed many debris objects, and SBP images revealed the subsurface structure around the north-eastern end of our study area. These features suggest a structure related to the formation of the latest submarine fan. However, a strong reflection layer exists below ~20 ms below the seafloor in the south-western area, which we interpret as a denudation feature, now covered with younger surface sediments. We continue to improve the vehicle's performance, and expect that many fruitful results will be obtained using URASHIMA.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.345-361
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• 2005

A transform-space index indexes objects represented as points in the transform space An advantage of a transform-space index is that optimization of join algorithms using these indexes becomes relatively simple. However, the disadvantage is that these algorithms cannot be applied to original-space indexes such as the R-tree. As a way of overcoming this disadvantages, the authors earlier proposed the transform-space view join algorithm that joins two original- space indexes in the transform space through the notion of the transform-space view. A transform-space view is a virtual transform-space index that allows us to perform join in the transform space using original-space indexes. In a transform-space view join algorithm, the order of accessing disk pages -for which various space filling curves could be used -makes a significant impact on the performance of joins. In this paper, we Propose a new space filling curve called the adaptive row major order (ARM order). The ARM order adaptively controls the order of accessing pages and significantly reduces the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page) and the number of disk accesses for a given buffer size. Through analysis and experiments, we verify the excellence of the ARM order when used with the transform-space view join. The transform-space view join with the ARM order always outperforms existing ones in terms of both measures used: the one-pass buffer size and the number of disk accesses for a given buffer size. Compared to other conventional space filling curves used with the transform-space view join, it reduces the one-pass buffer size by up to 21.3 times and the number of disk accesses by up to $74.6\%$. In addition, compared to existing spatial join algorithms that use R-trees in the original space, it reduces the one-pass buffer size by up to 15.7 times and the number of disk accesses by up to $65.3\%$.

• Economic and Environmental Geology
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• v.49 no.6
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• pp.459-467
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• 2016

Due to the limited areal space for installation, borehole heat exchangers (BHEs) at depths deeper than 300 m are considered for geothermal heating and cooling in the urban area. The deep vertical closed-loop BHEs are unconventional due to the depth and the range of the typical installation depth is between 100 and 200 m in Korea. The BHE in the study consists of 50A (outer diameter 50 mm, SDR 11) PE U-tube pipe in a 150 mm diameter borehole with the depth of 300 m. In order to compensate the buoyancy caused by the low density of PE pipe ($0.94{\sim}0.96g/cm^3$) in the borehole filled with ground water, 10 weight band sets (4.6 kg/set) were attached to the bottom of U-tube. A thermal response test (TRT) and fundamental basic surveys on the thermophysical characteristics of the ground were conducted. Ground temperature measures around $15^{\circ}C$ from the surface to 100 m, and the geothermal gradient represents $1.9^{\circ}C/100m$ below 100 m. The TRT was conducted for 48 hours with 17.5 kW heat injection, 28.65 l/min at a circulation fluid flow rate indicates an average temperature difference $8.9^{\circ}C$ between inlet and outlet circulation fluid. The estimated thermophysical parameters are 3.0 W/mk of ground thermal conductivity and 0.104 mk/W of borehole thermal resistance. In the stepwise evaluation of TRT, the ground thermal conductivity was calculated at the standard deviation of 0.16 after the initial 13 hours. The sensitivity analysis on the borehole thermal resistance was also conducted with respect to the PE pipe diameter and the thermal conductivity of backfill material. The borehole thermal resistivity slightly decreased with the increase of the two parameters.