• Journal of Korea Port Economic Association
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• v.35 no.2
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• pp.109-126
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• 2019

The Panmunjom Declaration adopted by the leaders of South and North Korea on April 27, 2018, has created an environment conducive for peace and cooperation in the Korean Peninsula. In the June of last year, South Korea has joined the Organization for Cooperation between Railways (OSJD). The membership of OSJD has established a solid foundation for restoring a multimodal logistics system that connects the Korean peninsula to Eurasia countries, including China and Russia. In this paper, a questionnaire survey targeting working-level experts was conducted to find the barriers in constructing multimodal logistics that efficiently connect the port-continental railways of the Korean peninsula and the Eurasian nations. Survey items were divided into five categories-border crossing procedures, technology, facilities, operation, and government support. As a result, among the most important problems of international multimodal logistics in Eurasia that need to be solved on priority include improving transshipment facilities, eliminating inspection carried out at every country for transit, simplifying documents for customs clearance, and minimizing the changes in freight rates. In conclusion, for vitalizing the connection between the Korean peninsula and the continental railways, it is necessary to develop a transshipment system to facilitate the changes in tracks at the borders by making a joint effort with the international community. Second, railway and operational systems in South Korea, North Korea, China, and Russia should be standardized. Third, international cooperation among South Korea, North Korea, China, and Russia is essential for simplifying customs clearance at borders, priority departure of domestic cargo, sharing information about the changes in freight rates, and so on. Finally, the government should come up with measures to secure the quantity of cargo required to form block trains, while developing new business models.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.470-476
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• 2020

In this paper, we present the design of modular replaceable AI server for image deep learning that separates the server from the Edge Device so as to drive the AI block and the method of data transmission and reception. The modular replaceable AI server for image deep learning can reduce the dependency between social robots and edge devices where the robot's platform will be operated to improve drive stability. When a user requests a function from an AI server for interaction with a social robot, modular functions can be used to return only the results. Modular functions in AI servers can be easily maintained and changed by each module by the server manager. Compared to existing server systems, modular replaceable AI servers produce more efficient performance in terms of server maintenance and scale differences in the programs performed. Through this, more diverse image deep learning can be included in robot scenarios that allow human-robot interaction, and more efficient performance can be achieved when applied to AI servers for image deep learning in addition to robot platforms.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.287-292
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• 2021

Load and resistance factor design is an efficient design approach that provides a system of consistent design solutions. This study aims to determine the load and resistance factors needed for the design of breakwater foundations within a probabilistic framework. In the study, four typical types of Korean breakwaters, namely, rubble mound breakwaters, vertical composite caisson breakwaters, perforated caisson breakwaters, and horizontal composite breakwaters, are investigated. The bearing capacity of breakwater foundations under wave loading conditions is thoroughly examined. Two levels of the target reliability index (RI) of 2.5 and 3.0 are selected to implement the load and resistance factors calibration using Monte Carlo simulations with 100,000 cycles. The normalized resistance factors are found to be lower for the higher target RI as expected. Their ranges are from 0.668 to 0.687 for the target RI of 2.5 and from 0.576 to 0.634 for the target RI of 3.0.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.1-16
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• 2022

The location of the bus stop on urban roads should be installed at a point where it is convenient for users and the impact of bus stops on the traffic flow is minimized. However, the location of the bus stops is determined indiscriminately due to the lack of related research. Therefore, this study developed a traffic accident prediction model and calculated the proper separation distance for the bus stops through an optimization technique. The result of the study indicates that the bus stop can be installed in the form of a mid-block approximately 87 to 166 m away from the intersection in the road section. This result is valid if the number of main road lanes in the road section is 2 to 4 with a level of traffic from 1,000 to 3,000 v/h. In the section with 5 to 6 lanes, it is desirable to install a bus stop close to the intersection by about 42 to 97 m.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.11-17
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• 2022

Currently, the income and funds of the local economy are flowing out to the metropolitan area, and talented people, the driving force for regional development, also gather in the metropolitan area, and the local economy is facing a serious crisis. Local currency is issued by local governments and is a currency with auxiliary and complementary functions that can be used only within the area concerned. In order to revitalize the local economy, as local governments have focused their attention on the introduction of local currency, studies on the issuance and use of local currency are continuously being conducted. In this study, by using big data from data materials such as portals and SNS, the consumer image of local currency issued in local governments was identified through big data analysis, and based on the research results, the issuance and operation of local currency was conducted. The purpose is to present implications for The results of this study are as follows. First, by inducing local consumption through the policy issuance of local currency, it is showing the effect of increasing the economic income of the region. Second, local governments are exerting efforts to revitalize the economy and establish a virtuous cycle system for the local economy by issuing and distributing local currency. Third, the introduction of blockchain technology shows the stable operation of local currency. With academic significance, it was possible to grasp the changed appearance and effect of local currency through big data analysis and the policy direction of local currency.

• Journal of Korea Foundry Society
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• v.41 no.4
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• pp.349-356
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• 2021

Effects of cooling condition after solidification on the microstructure and the mechanical properties of 0.36Mn containing ductile cast iron have been studied based on the minimized addition of Cu and Sn for vehicle component applications with better quality and cost competitiveness. Cu and Sn were selected for additional elements judging from the well-known fact of strong tendency of pearlite promotion followed by the tensile property improvement. After pouring of the Mg treated cast iron melt with various chemical compositions into the block specimens, two ways of post solidification cooling conditions were applied for comparison; both cooling in the mold and cooling in the air after dismantle at 800℃. The pearlite fraction of the mold-cooled specimens was analyzed as 27-44%, with the tensile strength and elongation of 513-568N/mm² and 10.4-14.3%, respectively. Whilest, the air cooled specimens showed the pearlite fraction of 77~85%, with the tensile strength and elongation of 728~758N/mm² and 3.2~6.0%, respectively. It is worthwhile to note that the remarkable improvement of both tensile strength and elongation of the ductile iron was achieved by the present air cooling condition with the minimized combined addition of Cu and Sn to the 0.36Mn containing ductile iron.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.333-345
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• 2021

Amid the rapidly changing logistics environment and demand changes in the post-corona-19 era, the importance of the cold chain logistics sector is being highlighted. The scope of cold chain is not limited to food, but is expanding to various fields such as pharmaceuticals, semiconductors, and flowers. The demand on the storage and transportation of corona vaccines is rapidly increasing. The rapid increase in domestic low-temperature facility construction and renovation may lead to the saturation of the cold chain related industry in the future and slow growth. In preparation for this, it is necessary to accumulate infrastructure know-how using IT technologies, and to consider entering into the UN procurement market as a potential niche market, by taking advantage of Korea's recent global status. The demand for cold chain in the UN procurement market is increasing mainly in underdeveloped countries, and it is expected to continue to grow. In this paper, the capabilities of domestic cold chain related companies were analyzed, domestic and overseas cold chain logistics market trends and overseas market entry status were investigated. An in-depth survey was conducted to present strategies for domestic cold chain logistics related companies to enter the UN procurement market.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.