• Journal of the Korean Institute of Navigation
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• v.11 no.2
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• pp.1-31
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• 1987

Nowadays, the transportation of almost all cargoes depends on sea routes in international trade. In the transaction of trade, cargo transportation must be completed on the base of two contrary objectives, one of which is to protect the vessel, cargoes and crew aborad her safely through every step of the transportation and the other is to pursue profits from the transaction of the trade. In spite of the great development of the modern techniques in shipbuilding today, many sea disaters of big merchant vessels have been occurring successively in winter seasons every year on the sea routes of the North Pacific Ocean. Whenever the accident of losing a vessel in rough sea occurred , many experts of the country to which the vessel belonged had tried to take out the reason of the missing without manifesting the exact cause of the unhappy occurrence. In this paper, we calculated ocean wave status along the route of the North Pacific Ocean theoretically concluded by us as optimum on the basis of weather and sea conditions. In the calculation, we used ITTC wave spectrum formula and meteorological data of "Winds '||'&'||' Waves of the north Pacific Ocean" edited by Ship Research Institute of Japan on the basic data assembled by World Meterological Organization through past 10 years. We selected three sample vessels of most common size in the North Pacific Ocean Routes, a container, a log carrier and a bulk carrier and applied tree sample vessels to the calculated sea conditions for getting the rolling angles of the vessels and stress exerting on the hulls. Examining the calculated results, we concluded as follows; 1. Under the condition of these status7 by beaufort scale, "heave to" maneuvering is the best and safest way to steer every vessel. 2. The most dangerous part of sea area along the west bound optimum route of the North Pacific Ocean in winter season, is the southern sea area of the Kamchatka peninsula.a peninsula.

• Asian review of World Histories
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• v.4 no.1
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• Journal of Distribution Science
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• v.13 no.7
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• pp.33-40
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• 2015

Purpose - This study aims to find out what impacts large retailers' behaviors appearing when they promote the strengthening of their market dominating power in the trade relations with small and medium suppliers or in the market can have on consumers. Research design, data, methodology - This study analyzed negative information (news) on large retailers (Lotte Mart, E-Mart and Homeplus) based on the monthly data over the past five years from 2008 to 2012 and also analyzed the correlation between dependent variables that are likely to affect sales through large retailer economic index, Results - This study conducted a correlation analysis on the time lag of the factors that have an impact on the negative information and sales of large retailers in order to analyze how consumers respond to the choice of large retailers' store (store sales) when they perceived negative information about the un- ethical behaviors of large retailers. Conclusions - Unfair and negative information on large retailers appeared significant for the hypothesis that sales will be affected by the image of large retailers and change of consumer attitudes.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.87-95
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• 2004

Economic development is remarkable in Asia and progress of industrialization of NIES, ASEAN, and China in East Asia has increased the international physical distribution in this area. However, an imbalance of trade becomes severe in these areas. The imbalance is especially big in the Asia-North America route and the Japan-China route. The imbalance in the Asia -North America liner route is 5.04 million TEUS in 2002.The transportation ratio of loaded containers between China and Japan route is approximately 3:1 in 2000. In other words, it means that the transportation of loaded containers from China to Japan is 3, the transportation of loaded containers from Japan to China is I. The imbalance at a port is generally obtained by subtracting export loaded container cargo volume from import container cargo volume. However, the imbalance and the empty containers at the port are not always same. Then, in order to evaluate rationalization and efficiency of maritime container transportation, we introduce the amount of empty containers at a port as an evaluation index. However, the past data of the amount of handling empty containers have a lot of lacking portions. Then, it is necessary to estimate the past amount of empty containers in order to grasp the amount of empty containers historically. So, we construct the model that estimates the amount of empty containers using the imbalance of main port statistics in Japan.

• Knowledge Management Research
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• v.24 no.3
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• pp.95-125
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• 2023

As the aviation industry has rapidly been grown, there are more factors for customers to consider when choosing an airline. In response, airlines are trying to increase customer value by providing high-quality services and differentiated experiential value. While early customer value research centered on utilitarian value, which is the trade-off between cost and benefit in terms of utility for products and services, the importance of experiential value has recently been emphasized. However, experiential value needs to be studied in a specific context that fully represents customer preferences because what constitutes customer value changes depending on the product or service context. In addition, customer value has an important influence on customers' decision-making, so it is necessary for airlines to accurately understand what constitutes customer value. In this study, we collected customer reviews and ratings from Skytrax, a website specializing in airlines, and utilized the BERTopic technique to derive factors of customer value. The results revealed nine factors that constitute customer value in airlines, and six of them are related to customer satisfaction. This study proposes a new methodology that enables a granular understanding of customer value and provides airlines with specific directions for improving service quality.

• Journal of Korea Trade
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• v.25 no.3
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• pp.153-168
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• 2021

Purpose - In this study, we aim to explore new distribution strategies for sustainable growth in the era of the 4th industrial revolution, focusing on SMEs (small and medium-sized enterprises) in Korea, and suggest ways to upgrade the government's official distribution channel to the next level. Design/methodology - First of all, this paper explored the prior research, the current status of sales support for SMEs, and the changes in the distribution industry due to COVID-19 pandemic. Based on Moon (2016)'s ABCD strategic model - Agility, Benchmarking, Convergence, and Dedication, the study then derived directions in which official distribution channels should move and the new distribution strategy for Korean SMEs to secure competitive advantage. Findings - First, in terms of 'Agility', in order to upgrade official distribution channels, which are currently at some competitive disadvantages compared to private distribution companies, we must quickly introduce technologies for the 4th industrial revolution, such as AI, Big Data, etc., and establish precise strategies to strengthen the capabilities of SMEs. Second, in terms of 'Benchmarking', the use of "Chamelezones" has been increasing to enhance the competitiveness of offline stores in line with recent ontact trends. Therefore, official distribution channels should also benchmark such cases, strengthening their competitiveness by utilizing offline spaces more efficiently and effectively. Third, in terms of 'Convergence', in line with the rapidly changing trend of the times, official distribution channels should also promote active partnerships with media commerce, e-commerce and ICT platforms, as well as cooperation with private retailers, and focus on creating synergy effects through them. Finally, from the perspective of 'Dedication', digitalization should be promoted step by step, finding the sector that can accelerate digital among the value chains of official distribution channels, and continuing to discuss how to digitize it realistically. Originality/value - Based on this analysis, we have presented strategies and implications for innovating official distribution channels for SMEs, which will contribute to enhancing the competitive advantage of official distribution channels in the post COVID-19 pandemic era.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.47-59
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• 2012

A number of organizations have had big interests in studies concerning leadership and in academic areas, in not only management but also psychology. Until now, leadership has been accentuated by managers or team leaders especially. Recently, however, the concept of self-leadership directing one's own activities through self-control or self-management is being focused on in practices and in academia. This study is to investigate the influence between self-leadership strategies and learning performance in IT classes mediated by attitude of attendance focused on the social science students in two universities (Korea (121 samples) and India (106 samples)). And this research tried to compare difference between two university students. Research results can give us direction of task-taking attitudes in firms or learning attitudes in teaching organizations and implications to human resource managers who are in charge of improving learning performance or productivity.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.110-117
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• 2021

The development of core technologies in the 4th Industrial Revolution, such as artificial intelligence, big data, and the intelligent Internet of Things, promote digital transformation and intelligence of the manufacturing industry. To realize them, there is an increasing demand for materials, components, and equipment needed for final goods. In particular, the expansion of global value chain instability due to changes in the external environment, such as the U.S.-China trade dispute, Japan's export regulations, and Covid-19 pandemic, increases the importance of strengthening the materials, components, and equipment industry in the global market. Thus, this study presents a strategic direction for securing global industrial competitiveness of materials, components, and equipment using Michael Porter's diamond model approach.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.