• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.78
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• pp.93-117
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• 2018

In all countries of the world, the development of trade is an important factor for the survival of the national economy. Increased export will lead to national economic growth. Export is directly linked to employment, and the industrial structure will be developed in the direction to produce products of comparative advantages. Therefore, every country around the world is trying to promote export regardless of the size of its economy. Accordingly, this paper focused on the promotion of export of domestic companies. It proposed to cultivate trade experts to promote export of domestic companies. The following five methods were proposed to materialize the proposal. First, it is important to foster trade experts to expand and foster the one-person creative companies. In particular, it is important to develop a professional education curriculum. It is necessary to design and conduct a systematic curriculum throughout the process including follow-up after education such as teaching detailed procedures for establishing a trade business, identification of relevant regulations and related organizations, understanding of special features of each exporting country, and details of exporting procedures through specialist training for the individual industries, helping themto keep their network steady so that they can easily get help from consultants. Second, it is necessary to educate traders working in the field to make them trade experts and utilize themin on-the-job training and consulting. To do this, it is necessary to introduce systematic consultant selection process, and to introduce a systemto educate and manage them. It is because, we must select the most appropriate candidates, educate themto be lecturers and consultants, and dispatch themto the field, in order to make the best achievement in export. Nurturing trading professionals utilizing the current trading workers to activate export of domestic companies can be more efficient through cooperation of trading education agencies and related agencies in various industries. Third, it is also proposed to cultivate female trade experts by educating female trade workers whose career has been disrupted. It is to provide career disrupted women with opportunities to work after training them as trade professionals and to give manpower pool to domestic companies that are preparing for export. Fourth, it is also proposed to educate foreign students living in Korea to be trading experts and to utilize them as trading infra. They can be trading professionals who will contribute to the promotion of export. In the short term, they will be provided with opportunities for employment and start-upin the field of trade, and in the mid- to long-term, they may develop a business network between Korea and their own countries. To this end, we need to improve the visa system, expand free trade education opportunities, and support them so that they can establish small but strong enterprises. Fifth, it is proposed to proactively expand trade education to specialized high school students. Considering that most of domestic companies pursuing activation of export are small but strong companies or small and mediumsized companies, they may prefer high school graduates rather than university graduates because of financial limitations. Besides, the specialized high school students may occupy better position in the job market if they are equipped with expertise in trading. This study can be meaningful, in that it is the first research that focuses on cultivating trading experts to contribute to the export activation of domestic companies. However, it also has a limitation that it has failed to reflect the more specific field voices. It is hoped that detailed plans will be derived from the opinions of the employees of domestic companies making efforts to become an export company in the related researches in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.61-73
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• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.15-23
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• 2023

In a situation where the necessity and importance of ESG management is increasing recently, it is judged that selecting important ESG-related variables for container terminals, which are the bases of export and import logistics, among various variables of ESG evaluation agencies will help to establish ESG management strategies for container terminals which led us to proceed with this study. The results of word network analysis are summarized as follows. The weighed degree, that is, the AWD of Environmental management(E) variables, is obtained in the order of Environmental Protection Investment(54), Environmental Awareness Education(45), Work Team Structure(31), Environmental certification(32). Page Ranks, the order of centrality and connectivity index is Environmental Awareness Education(0.0765), Employee Engagement(0.0765), Environmental Protection Investment(0.0761), Work Team Composition(0.0761), and Environmental certification(0.0761). The AWD(Average Weighed Degree) of the Social Responsibility Management(S) variables, followed by Protecting workers' human rights and contributing to local communities(68), Safety Education(63), Safety certification(59), and Responding to infectious diseases(40). Orders by Page Ranks, centrality and connectivity Index, are Protecting workers' human rights and contributing to local communities(0.165), Safety Education(0.153), Safety Certification(0.144) and Responding to infectious diseases(0.102). The AWD of Governance and Ethical management(G) variables, followed by Anti-corruption(27), Transparent management(24), Mutual cooperation between stakeholders(19), and Sustainability reporting(9). Page Ranks, the order of centrality and connectivity index is the Anti Corruption(0.241), Transparent management(0.216), Mutual cooperation between stakeholders(0.174), Directors' roles and responsibilities(0.105), Shareholder protection(0.097) and Sustainability Report(0.096).

• Tunnel and Underground Space
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• v.30 no.4
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• pp.320-334
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• 2020

This study presents an application of hydro-mechanical coupled Particle Flow Code 3D (PFC3D) to simulation of fluid injection induced fault slip experiment conducted in Mont Terri Switzerland as a part of a task in an international research project DECOVALEX-2019. We also aimed as identifying the current limitations of the modelling method and issues for further development. A fluid flow algorithm was developed and implemented in a 3D pore-pipe network model in a 3D bonded particle assembly using PFC3D v5, and was applied to Mont Terri Step 2 minor fault activation experiment. The simulated results showed that the injected fluid migrates through the permeable fault zone and induces fault deformation, demonstrating a full hydro-mechanical coupled behavior. The simulated results were, however, partially matching with the field measurement. The simulated pressure build-up at the monitoring location showed linear and progressive increase, whereas the field measurement showed an abrupt increase associated with the fault slip We conclude that such difference between the modelling and the field test is due to the structure of the fault in the model which was represented as a combination of damage zone and core fractures. The modelled fault is likely larger in size than the real fault in Mont Terri site. Therefore, the modelled fault allows several path ways of fluid flow from the injection location to the pressure monitoring location, leading to smooth pressure build-up at the monitoring location while the injection pressure increases, and an early start of pressure decay even before the injection pressure reaches the maximum. We also conclude that the clay filling in the real fault could have acted as a fluid barrier which may have resulted in formation of fluid over-pressurization locally in the fault. Unlike the pressure result, the simulated fault deformations were matching with the field measurements. A better way of modelling a heterogeneous clay-filled fault structure with a narrow zone should be studied further to improve the applicability of the modelling method to fluid injection induced fault activation.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.65-78
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• 2006

This paper investigates the cost structure ot the Korea and Japan railroad industry with respect to density, scale and scope economies as well as productivity growth rate using a generalized trans)og multiproduct cost function model. The paper then assumes that the Korea and Japan railway companies pi·educe three outputs (incumbent railway passenger-kilometers. Shinkansen passenger-kilometers, ton-kilometers of freight) using four input factors (labor, fuel, maintenance, rolling stock and capital). The specified cost function includes foul other independent variables: track lengths to reflect network effects, two dummies to reflect nation and ownership effects, and time trend as a proxy for technical change. The simultaneous equation system consisting of a cost function and three input share equations is estimated with the Zellner's iterative seemingly unrelated regression. The unbalanced panel data used in the paper, a total of 154 observations. are collected from the annual records of the Korea National Railroad (KNR) for the yews $1977{\sim}2003$, Japan National Railways (JNR) for the years $1977{\sim}1984$. seven Japan Railways (JR's) for the years $1987{\sim}2003$. The findings show that the Korean and Japanese railways exhibit product-specific and overall economies of density but product-specific diseconomies of scale with respect to incumbent railway passenger-kilometers, Shinkansen-kilometers and ton-kilometers. However, the railways experience mild overall economies of scale which result from economies of scope associated with the joint production of incumbent railway/Shinkansen and feight, freight/incumbent railway and Shinkansen except Shinkansen/incumbent railway and freight. In addition, the economies of density and scale in the KNR, JR east, JR central, and JR west companies at the point of the years $1990{\sim}2003$ average is generally analogous to the above results at the point of sample average. There also appear to be economies of ssope associated with the joint Production of the incumbent railway and Shinkansen in JR central but diseconomies of scope in JR East and JR West. The findings also indicate that the productivity growth rate of the privately-owned JR's is larger than that of the government-owned KNR.

• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.209-225
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• 1997

Today, rapid progress of urbanization is discovered commonly in many countries, especially in developing countries, which has led to spatial order and development process of city. Historically, Taegu was a walled city and formed mono-nucleus which was restricted by the castle. As the city grew gradually, the castle was removed as a result of diversification in traffic network, change of socio-economic environment, formation of industrial base and functional distribution. According to reconstruction maps of residential patterns, there was distinctive residential segregation among ethnic groups. Koreans in Taegu in 1939, aggregated densely in the southern and western parts of the city. The Japanese were concentrated densely in the northern and eastern parts of Taegu. And the street pattern within residential areas of the Korean people was shaped like a maze type in contrast with Japanese residential areas, which showed grid pattern of streets. This is another general pattern of almost all colonial cities, especially in Asia. Through this process, today it appears that, out of overall residential areas which occupy the highest ratio in urban land use, those for eminent people influence the functional development of urban spatial structure very heavily as a key point in urban residetial structure. Truly, residential segregation can be seen as the spatial manifestation of uneven distribution of such important scarce resources as housing and residential environment. In this study, the characteristics of locational distribution of the eminent people show their socially and economically stabilized standing in Taegu, taking the aforesaid situation as a background of the study. And the process of this study is as follows ; to examine the forming process of residential areas in the city as a theoretical supporting, to put in order on classical interpretation to formation of residential areas, and general type modern residential areas formation, and economic decision factor of land use. Therefore, this study aims to examine growth and development of eminent persons' residential areas and, at the same time, extract locational characteristics through the pattern of eminent persons' location and predict changes in the future.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.661-683
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• 2001

There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage video databases efficiently. The development of high speed data network and digital techniques has emerged new multimedia applications such as internet broadcasting, Video On Demand(VOD) combined with video data processing and computer. Video database should be construct for searching fast, efficient video be extract the accurate feature information of video with more massive and more complex characteristics. Video database are essential differences between video databases and traditional databases. These differences lead to interesting new issues in searching of video, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of video. In this paper, We propose the construction and generation method of the video database based on contents which is able to accumulate the meaningful structure of video and the prior production information. And by the proposed the construction and generation method of the video database implemented the video database which can produce the new contents for the internet broadcasting centralized on the video database. For this production, We proposed the video indexing method which integrates the annotation-based retrieval and the content-based retrieval in order to extract and retrieval the feature information of the video data using the relationship between the meaningful structure and the prior production information on the process of the video parsing and extracting the representative key frame. We can improve the performance of the video contents retrieval, because the integrated video indexing method is using the content-based metadata type represented in the low level of video and the annotation-based metadata type impressed in the high level which is difficult to extract the feature information of the video at he same time.