• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.4
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• pp.67-88
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• 2021

The National Science and Technology Standard Classification System is established and operated for the purpose of efficiently managing science and technology related information, manpower, and R&D projects. The revision cycle for the classification system is five years, and 2022 is the first year of the next revision procedures. The main purpose of the next revision is to turn the third level categories into the keywords in the current classification system. It is to solve the problems about not reflecting the latest terms, and the difficulty in linking with the relevant other classifications caused by the rigid structure of the current classification system. In this study, the method was proposed by changing the existing classification system into the term management system as to improve the quality and usability of keywords related with the current third level categories. For this method, SKOS, the international standard terminology management system, and ISO/IEC 11179 standards were offered as basic models. In addition, the related metadata standards used in overseas scientific and technological glossaries were investigated, and compared with the current National Science and Technology Standard Classification System. And then essential metadata elements from the terminology management as point of view was derived. As a result, 11 standard metadata elements that can be immediately modified and applied in the current system were recommended, and five elements that can be applied after the revision of the classification system were offered.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.610-622
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• 2021

The DECOVALEX project is one of the representative international cooperative projects to enhance the understanding of the complex Thermo-Hydro-Mechanical-Chemical(THMC) coupled behavior in the high-level radioactive waste disposal system based on the numerical simulation. DECOVALEX-2023 is the current phase consisting of 7 tasks, and Task C aims to model the THM coupled behavior in the disposal system based on the Full-scale Emplacement (FE) experiment at the Mont-Terri underground rock laboratory. This study performs the numerical simulation based on the OGS-FLAC developed for the current study. In the numerical model, we emplaced the heater with constant power horizontally based on the FE experiment and monitored the pressure development, temperature increase, and mechanical deformation at the specific monitoring points. We monitored the capillary pressure as the primary effect inducing the flow in the buffer system, and thermal stress and pressurization were dominant in the surrounding rocks' area. The results will also be compared and validated with the other participating groups and the experimental data further.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.4
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• pp.243-248
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• 2022

Weight optimization was performed for a rotorcraft shaft system using one-dimensional Euler-Bernoulli beam elements. Torsion, shaft support stiffness such as bearings, flange mass are all considered. To guarantee structural dynamic stability, eigenvalue analysis was performed to avoid critical speed and tooth mesh excitation form the gearbox. The weight optimization was performed by adjusting the thickness and radius while the length of the shaft was fixed, and the optimization process was divided into two stages. In the first, the weight is optimized with the torsional strength constraint. In the second, the difference between the primary mode of shaft and the critical speed is maximized so that the primary mode of the shaft can avoid the critical speed while the constraint on the torsional strength of the shaft is satisfied according to the standard for shaft system stability (AMC P 706-201, 1974). The proposed method was verified by comparing the results of the optimal design using the given one-dimensional beam elements with the stress results of the 3D finite element and the actual manufactured shaft.

• Journal of the Korean GEO-environmental Society
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• v.22 no.12
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• pp.5-13
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• 2021

Expandable steel pipe piles have been developed to ensure stability and reduce construction costs during underground floor remodeling and extension work. Expandable steel pipe piles are more economical and stable than micropiles. Extensible steel pipe pile is a method of improving the performance of steel pipes by expanding steel pipes underground. In this paper, the changes in buckling strength according to the shape of steel pipes in an extended steel pipe pile were identified, a numerical analysis model was developed to determine the expended part effect of bumps due to steel pipe expansion, and the optimal steel pipe expansion was calculated through material tests. The larger the expansion diameter of the steel pipe and the greater the number of expanded part, the greater the buckling strength. Numerical results showed that the number of expanded part has a greater effect on buckling strength than the expansion rate. When the expansion rate is more than 1.2 times, it can be seen that as the number of expanded part increases, the effect of increasing buckling strength increases significantly. It was also noted that the expanded part effect of the bumps occur significantly when the extension angle is less than 45° and the expansion rate is 1.3 times higher. When the steel pipe is failure, the expanded rate is 20 to 32%, averaging 25.4%. Through the material test, it was analyzed that it is desirable to limit the maximum expansion rate for performing steel pipes to 16%.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.685-690
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• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.447-455
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• 2017

At the modern industry, the composite material has been widely used. Particularly, the material of carbon fiber reinforced plastic hardened with resin on the basis of fiber is excellent. As the specific strength and rigidity are also superior, it receives attention as the light material. Among these materials, the carbon fiber reinforced plastic using carbon fiber has the superior mechanical property different from another fiber. So, it is utilized in vehicle and airplane at which high strength and light weight are needed at the same time. In this paper, the tensile property due to the fiber design is investigated through the analysis study with CT specimen composed of carbon plastic reinforced plastic. At the stress analysis of CFRP composite material with hole, the fracture trend at the tensile environment is examined. Also, it is shown that the lowest stress value happens and the deformation energy of the pre-crack becomes lowest at the analysis model composed of the stacking angle of 60° through the result due to the stacking angle. On the basis of this study result, it is thought to apply the foundation data to anticipate the fracture configuration at the structure applied with the practical experiment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1A
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• pp.1-13
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• 2010

This paper presents an investigation on the geometric nonlinear behavior of cable-stayed bridges using geometric nonlinear finite element analysis method. The girder and mast in cable-stayed bridges show the combined axial load and bending moment interaction due to horizontal and vertical forces of inclined cable. So these members are considered as beam-column member. In this study, the nonlinear finite element analysis method is used to resolve the geometric nonlinear behavior of cable-stayed bridges in consideration of beam-column effect, large displacement effect (known as P-${\delta}$ effect) and cable sag effect. To analyze a cable-stayed bridge model, nonlinear 6-degree of freedom frame element and nonlinear 3-degree of freedom equivalent truss element is used. To resolve the geometric nonlinear behavior for various live load cases, the initial shape analysis is performed for considering dead load before live load analysis. Then the geometric nonlinear analysis for each live load case is performed. The deformed shapes of each model, load-displacement curves of each point and load-tensile force curves for each cable are presented for quantitative study of geometric nonlinear behavior of cable-stayed bridges.