• Economic and Environmental Geology
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• v.55 no.4
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• pp.399-406
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• 2022

In line with the megatrend of 2050 carbon neutrality, the amount of critical minerals used in clean-energy technology is expected to increase fourfold and sixfold, respectively, according to the Paris Agreement-based scenario as well as the 2050 carbon-neutrality scenario. And, in the case of Korea, in terms of the battery supply chain used for secondary batteries, the midstream that manufactures battery materials and battery cell packs shows strength, but the upstream that provides and processes raw materials is experiencing difficulties. The Korea Institute of Geoscience and Mineral Resources has established a strategy to secure lithium, nickel, and cobalt and is conducting surveys to respond to the upstream risk of these types of battery raw materials. In the case of lithium, exploration has been carried out in Uljin, Gyeongsangbuk-do since 2020, and by the end of 2021, the survey area was selected for precision exploration by synthesizing all exploration data and building a 3D model. Potential resources will be assessed in 2022. In the case of nickel, the prospective site will be selected by the end of 2022 through a preliminary survey targeting 10 nickel sulfide deposits that have been prospected in the past. In the case of cobalt, Boguk cobalt is known only in South Korea, but there is only a record that cobalt was produced as a minor constituent of hydrothermal deposit. According to the literature, a cobalt ore body was found in the contact area between serpentinite and granite, and a protocol for cobalt exploration in Korea will be established.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• Asia Marketing Journal
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• v.13 no.4
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• pp.253-264
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• 2012

A growing number of Korean companies are trying to expand their business area into global market due to saturation in the Korean domestic market. Home shopping industry arriving on mature stage is faced with less growth recently. CJ O Shopping which is a top ranked home shopping company in Korea, has been showing meaningful performances by earlier moving to global market with thorough preparations. CJ O Shopping's global marketing strategy focused on asian countries including China, India, Vietnam, and Japan is going successfully, which enables top ranked on-line retailing company in asia as well as in Korea. CJ O Shopping effectively penetrated into overseas market with both core competence based on Korean home shopping model and rigorous preliminary study on target market. Especially shoppertainment (Shopping+Entertainment) that is unique feature of globally competitive Korean home shopping created huge differentiations in target market. Also choosing the influential local partner, sharing the business goals, and building the joint venture could make stable operations, thereby easily earning of well-established awareness from target consumers. A step ahead entry of competitors and intensive localization of CJ O Shopping's core competence for arriving safe in target market were additional key factors for global marketing success. We can extract above key factors for success as implications of case study on CJ O Shopping's global marketing, and expect those factors to be spread into lots of Korean companies and utilized as successful strategies for global marketing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.883-890
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• 2022

Purpose: Currently, in the case of domestic fire fighting facility design, it is difficult to secure highquality manpower due to low design costs and overheated competition between companies, so there is a limit to improving the fire safety performance of buildings. Accordingly, AI-based firefighting design solutions were studied to solve these problems and secure leading fire engineering technologies. Method: Through AutoCAD, which is widely used in existing fire fighting design, the procedures required for basic design and implementation design were processed, and AI technology was utilized through the YOLO v4 object recognition deep learning model. Result: Through the design process for fire fighting facilities, the facility was determined and the drawing design automation was carried out. In addition, by learning images of doors and pillars, artificial intelligence recognized the part and implemented the function of selecting boundary areas and installing piping and fire fighting facilities. Conclusion: Based on artificial intelligence technology, it was confirmed that human and material resources could be reduced when creating basic and implementation design drawings for building fire protection facilities, and technology was secured in artificial intelligence-based fire fighting design through prior technology development.

• The Korean Journal of Archival Studies
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• no.75
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• pp.177-212
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• 2023

Private archives are an important indicator of understanding a society that contains various memories, the lives and experiences of members, daily lives, morality, and values. Recently, as diversity has emerged as an important value in Korean society, a number of individuals and communities have been appeared based on different bases and purposes, and the contents, types, and categories of private archives produced from their voluntary activities have also diversified. These private organizations and communities are potential targets for producing and holding private archives, but most of them do not have the minimum infrastructure or system for management of archives, and the foundation for management of archives is weak only to be supported with the voluntary will and activities of the private sector. Therefore, there is a need for a plan to support activities to manage archives suitable for each organization's level while respecting the unique characteristics and methods of the private sector within the national management system of archives. In addition, since it is difficult to solve all issues related to management of archives in the private sector with only a small number of process topics, a cooperative system should be established to sustain activities to manage archives on its own through networks between private sectors. In this study, we intend to propose a 'private archives information network platform (hereinafter referred to as a platform)' as a way to establish a communication and network foundation between private sectors and share resources with each other. Based on the results of analysis of cases of building network between private sectors and expected user requirements, we would like to establish a vision and target model of the platform and discuss ways to continuously operate the platform.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.23-34
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• 2009

In the railroad rolling stock depot, long-term maintenance tasks is done regularly every two or four year basis to maintain the functionality of equipments and rolling stock body or for the repair operation of the heavily damaged rolling stocks by fatal accidents. This paper addresses the computer simulation model building for the rolling stock maintenance shop for the CDC(Commuter Diesel Car) and Generator Car planned to be constructed at Daejon Rolling Stock Depot, which will be moved from Yongsan Rolling Stock Depot. We evaluated the processing capacity of two layout design alternatives based on the maintenance process chart through the developed simulation models. The performance measures are the number of processed cars per year, the cycle time, shop utilization, work in process and the average number waiting car for input. The simulation result shows that one design alternative outperforms another design alternative in every aspect and superior design alternative can process total 340 number of trains per year 15% more than the proposed target within the current average cycle time.

• Land and Housing Review
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• v.14 no.3
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• pp.93-106
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• 2023

This study attempted to simulate changes in the thermal environment according to the type of apartment complex in Korea using CFD techniques and evaluate the thermal environment by type of apartment. First, apartment complex types in the 2000s and 2010s were referred from previous studies and four types of apartment complex were extracted from. Second, the layout of the apartment complex and temperature changes were analyzed by the direction of wind inflow. Third, a standardized model was created from each type using tower type, plate type, and mixed driving. Last, CFD simulations were performed by setting up the inflow of wind from a total of eight directions. The temperature was relatively low in the type consisting of only the tower type and the type of placing the tower type in the center of the complex, regardless of the direction of the wind. It was due to the good inflow of wind from these types to the inside of the complex. It can be interpreted because wind flows easily into the complex in these types. The findings showed that wind flow and resulting temperature distribution patterns differed depending on the building type and complex layout type, confirming the need for careful consideration of the complex layout in the early design stage. The results are expected to be used as basic data for creating a sustainable residential environment in the early design stage of apartment complexes in the future.

• Land and Housing Review
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• v.14 no.3
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• pp.145-155
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• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.