• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1415-1425
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• 2018

MMORPG genre is based on the belief that all users in virtual world are equal. All users are able to obtain the corresponding wealth or status as they strive under the same resource, time. However, game bot is the main factor for harming this fair competition, causing benign gamers to feel a relative deprivation and deviate from the game. Game bots mainly form GFG(Gold Farming Group), which collects the goods in the game indiscriminately and adversely affects the economic system of the game. A general game bot detection algorithm is useful for detecting each bot, but it only covers few portions of GFG, not the whole, so it needs a wider range of detecting method. In this paper, we propose a method of detecting GFG based on items used in MMORPG genre. Several items that are mainly traded in the game were selected and the flows of those items were represented by a network. We Identified the characteristics of exchanging items of GFG bots and can identify the GFG's item trade network with real datasets from one of the popular online games.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1-7
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• 2007

New Zealand is a hydrologically diverse and active country. This paper presents an overview of the major hydrological issues and problems facing New Zealand and provides examples of some the research being undertaken to solve the problems. Fundamental to any environmental decision making is the provision of good quality hydrometric data. Reduced funding for the national hydrometric network has meant a reduction in the number of monitoring sites, the decision on how to redesign the network was made using information on geographic coverage and importance of each site. New Zealand faces a major problem in understanding the impacts of rapid land use change on water quantity and quality. On top of the land use change is overlain the issue of agricultural intensification. The transfer of knowledge about impacts of change at the small watershed scale to much larger, more complex watersheds is one that is attracting considerable research attention. There is a large amount of research currently being undertaken to understand the processes of water and nutrient movement through the vadose zone into groundwater and therefore understanding the time taken for leached nutrients to reach receiving water bodies. The largest water management issue of the past 5 years has been based around fair and equitable water allocation when there is increasing demand for irrigation water. Apart from policy research into market trading for water there has been research into water storage and transfer options and improving irrigation efficiency. The final water management issue discussed concerns the impacts of hydrological extremes (floods and droughts). This is of particular concern with predictions of climate change for New Zealand suggesting increased hydrological extremes. Research work has concentrated on producing predictive models. These have been both detailed inundation models using high quality LIDAR data and also flood models for the whole country based on a newly interpolated grid network of rainfall.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• International Commerce and Information Review
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• v.16 no.1
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• pp.207-226
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• 2014

A trade logistic informatization system under Open Systems Interconnection(OSI) includes a Port Management Information System, a Maritime Information System, and an Export and Import Batch Processing System. These have made a great contribution in the creation of more convenient and efficient management for the logistics industries in our country. However, this management is exposed to the technological problems of networks due to the explosive use in the sending and receiving of e-documents. For our country to grow as a center for port and logistic information, we should make the best use of the control systems using networks and further advance the export and import logistic systems. Therefore, this study aims to propose management systems for a composite network and an invasion detection system for efficient management of an e-trade network under OSI. Methods to rationalize the internal organizations such as coordination of organizations and human resources according to alloted network functions, commissions and arbitrary decisions, and reorganization of relevant regulations are not discussed here. This study looked at trade network under OSI from the aspect of practical business affairs and presented a basis for further interpretation.

• Information Systems Review
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• v.22 no.1
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• pp.59-72
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• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.677-693
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• 2016

Using Social Network Analysis and Trade in Value Added Database(TiVA), this paper examines the world trade network. Main findings are as follows. Firstly, there are three types of industries, which have dominant status in the world value added trade network. Those are the manufacturing industries in the developing countries such as China's electronics industry, the service industries in the developed countries such as U.S. R&D, and the manufacturing industries in the developed countries such as German motor vehicle industry. Secondly, the major hub industries in the world trade network have their own specific types in the brokerage roles. Most interestingly, U.S. service industries such as the R&D, the logistics industry, and the whole sale and retail industry reveal itinerant and liaison brokerage roles. Thirdly, Korean industries have been dominated by Chinese industries. However, the financial industry and the R&D industry could have revealed superior status as the brokerage role of itinerant. This implies Korean industries could sustain their competitiveness of the hubness status only by openness policy in the service industry.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.221-252
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• 2010

This study presents a concept of technology trade network and management, and proposes a procedural method for optimally selecting the technology transferor when a technology transferee needs to buy a specific technology. We develop a technology trade network where technology supplier, technology marketer, and technology transferee are informatively linked. And a technology trade management consists of three step of estimating technology, trading technology, and commercialization technology. Technology transferees could import the best appropriate technology which they want through these technology network method and cost optimization method. And we hope that these methodologies can be used in selecting new technology. A methodology can be classified into an estimating technology process and a choice of technology supplier process. In an estimating technology process, we calculate the technology similarity quantitatively through developing method of estimating technology which is focused on its technological characteristics. After defining the related cost of technology introduction, we suggest goal programming model to find a solution which can be acceptable both maximizing the technology similarity and minimizing the cost of technology. And suggested model is verified with a supplier selection problem of next generation tanks.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• Journal of the Korean Institute of Navigation
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• v.17 no.2
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• pp.57-73
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• 1993

Subject : The Practical Use of UN Standard Message for cargo flow EDI Writer : Park, Nam Kyu and Lee, Tae Woo It's necessary to prepare standard message which is agreed on among trading partners for EDI in container transport industry. Now KMPA is carrying out the EDI Project for establishing Korea Logistics Network. It is important to make standard message of documents using in transport industry to succesfully accomodate EDI. The objective of this study is to find out the method of UN standard message utility in Korea. For this study, the UN message guideline is primarily reviewed, and the process that Shipping Request being used in Hanjin Shipping Co. Ltd. is applied to UNSMs as case study. Generally the data format of EDIFACT is so complex and broad for inter-industry standard that the abstract of data format is usually used. Therefore, it is necessary to make the subset of standard message for Shipping Request in ocean industry. In the result of this study, that the ocean industry can use the subset of IFTMBF for Shipping Request is proved, and the subset is suggested. This thesis will contribute toward showing the practical way of standardrization of 350 documents using in trade, customs and transport sectors.