• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.47-54
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• 2019

Social dredging of various river facilities, such as dams and agricultural reservoirs currently being constructed, should be done to ensure stable reservoirs. However, it is difficult to find a system that tells the exact amount of water in real-time in a reservoir or dam. These measurements require an automated system to collect and analyze highly accurate data in real time. In this study, we propose a method to measure the amount of water and soil of reservoir in real time through multi-division volume calculation using a drone, and this method can detect sediment conditions in real time and determine the exact timing and scale of dredging.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1081-1089
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• 1999

Volume data modeling is concerned with finding a mathematical function which represents the relationship implied by the 3D data. Modeling a volume data geometrically can visualize a volume data using surface graphics without voxelization. It has many merits in that it is fast and requires little memory. We proposes, a method based on wavelet transformation and tetrahedrization. we implement a prototype system based on the proposed method. Last, we evaluated the proposed method comparing it with marching cube algorithm. the evaluation results show that though the proposed method uses only 13% of the volume data, the images generated is as good as the images generated by the marching cubes algorithm.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.350-362
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• 2002

A new architecture called the Storage Area Network(SAN) was developed in response to the requirements of high availability of data scalable growth, and system performance. In order to use SAN more efficiently, most SAN operating systems support storage virtualization concepts that allow users to view physical storage devices attached to SAN as a large volume virtually. A logical volume manager plays a key role in storage virtualization. It realizes the storage virtualization by mapping logical addresses to physical addresses. In this paper, we design and implement an efficient and flexible mapping method for logical volume manager. The nin method in this paper supports a snapshot that preserves a volume image at certain time and on-line reorganization to allow users to add or remove storage devices to SAN even while the system is running.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.9
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• pp.457-464
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• 2005

In this paper a 3D memory system that allows 17 access types at an arbitrary position is introduced. The proposed memory system is based on two main functions: memory module assignment function and address assignment function. Based on them, the memory system supports 17 access types: 13 Lines, 3 Rectangles, and 1 Hexahedron. That is, the memory system allows simultaneous access to multiple data in any access types at an arbitrary position with a constant interval. In order to allow 17 access types the memory system consists of memory module selection circuitry, data routing circuitry for READ/WRITE, and address calculation/routing circuitry In the point of view of a developer and a programmer, the memory system proposed in this paper supports easy hardware extension according to the applications and both of them to deal with it as a logical three-dimensional away In addition, multiple data in various across types can be simultaneously accessed with a constant interval. Therefore, the memory system is suitable for building systems related to ,3D applications (e.g. volume rendering and volume clipping) and a frame buffer for multi-resolution.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1633-1636
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• 2003

In this paper, we developed the scheme to grow to use newly added disk space without having to kill the application, unmount file system. This scheme, called online resizing, can resize the file system layout with the advent of Logical Volume Manager. The online resizing scheme is designed and implemented in linux cluster system where multiple hosts share the disk data in storage area network environment. It is incorporated with SANfs shared file system and can perform resizing technique with SANfs-VM volume manager. The experimental result shows that it can maximize the availability and capacity of the SANfs system which are important for modem servers where must not lose their customer.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.474-481
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• 2008

Scanning worm increases network traffic load and result in severe network congestion because it is a self-replicating worm and send copies of itself to a number of hosts through the Internet. So an early detection system which can automatically detect scanning worms is needed to protect network from those attacks. Although many studies are conducted to detect scanning worms, most of them are focusing on the method using packet header information. The method using packet header information has long detection delay since it must examine the header information of all packets entering or leaving the network. Therefore we propose an algorithm to detect scanning worms using network traffic characteristics such as variance of traffic volume, differentiated traffic volume, mean of differentiated traffic volume, and product of mean traffic volume and mean of differentiated traffic volume. We verified the proposed algorithm by analyzing the normal traffic captured in the real network and the worm traffic generated by simulator. The proposed algorithm can detect CodeRed and Slammer which are not detected by existing algorithm. In addition, all worms were detected in early stage: Slammer was detected in 4 seconds and CodeRed and Witty were detected in 11 seconds.

• IE interfaces
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• v.18 no.3
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• pp.234-246
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• 2005

A silicon wafer is a highly customized product made to the individual order varying its electrical and physical characteristics. Therefore, it has distinctive supply chain structure that is different from highly standardized commodity product. For high-volume/high-standardization product, it is general that a main stream of information flow initiated by the production planning of the manufacturers is usually directed to push both ways in a supply chain: upstream to the suppliers and downstream to the customers. Contrastingly, for low-volume/high-customization product, the information flow triggered by the fluctuating customer demand usually propagates upward to the suppliers through the manufacturers. Furthermore, for R &D based hi-technology product like silicon wafer, the interactive information feedback mechanism between manufacturer and customer, which is essential to the new product development process, is to be embedded in the supply chain. This article is a case study of supply chain management system of LG Siltron, a major Korean silicon wafer manufacturer. The SCM system entails special information structure fitting well typical high-variety/high-customization product, and also gives application possibilities to the R&D based high-technology product made to the individual customer order.

• Journal of KIISE:Information Networking
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• v.31 no.5
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• pp.438-449
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• 2004

With the growing deployment of network and internet, the importance of security is also increased. But, recent intrusion detection systems which have an important position in security countermeasure can't provide proper analysis and effective defence mechanism. Instead, they have overwhelmed human operator by large volume of intrusion detection alerts. In this paper, we propose an efficient alert analysis system that can produce high level information by analyzing and processing the large volume of alerts and can detect large-scale attacks such as DDoS in early stage. And we have measured processing rate of each elementary module and carried out a scenario-based test in order to analyzing efficiency of our proposed system.

• The Journal of Information Systems
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• v.21 no.4
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• pp.185-199
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• 2012

The most of international shipping cargoes are transported by ship and the shipping market has been continuously increasing. However, due to the recent global recession, the volume of trade decreased accordingly, and the competition between shippers to gain a competitive advantage in the shipping market continues deepening. To do this, researches on transportation costs savings as well as ways to improve customer service are needed and recently related researches are being pursued. Car carriers which transport more than 80% of car import and export volume in the world also make a lot of effort in order to reduce transportation costs, but in contrast related researches are seldom fulfilled. In this study, we developed an agent system that supports the efficient transportation of car carriers plan so that the car carriers can increase competitiveness through reduction of the logistics costs. To make this, we researched an algorithm for maritime transportation planning, and also developed a prototype of an agent system which can collect the necessary information in real time and establish a dynamic plan.

• 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.