• Journal of KIISE:Databases
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• v.30 no.4
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• pp.408-419
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• 2003

Data warehouses are usually dedicated to the processing of quires issued by decision support system(DSS). The response time of DSS queries is typically several orders of magnitude higher than the one of OLTP queries. Since DSS queries are often submitted interactively, techniques for reducing their response time are important. The caching of query results is one such technique particularly well suited to the DSS environment. In this paper, we present a cache manager for such an environment. Specifically, we define a canonical form of query. The cache manager looks up a query based on the exact query match or using a suggested query split process if the query is found is non-canonical form or in canonical form, respectively. It dynamically maintains the cache content by employing a profit function which reflects in an integrated manner the query execution cost, the size of query result, the reference rate, the maintenance cost of each result due to updates of their base tables, and the frequency of such updates. We performed the experimental evaluation and it positively shows the performance benefit of our cache manager.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.104-111
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• 2012

It is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.579-584
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• 2024

Currently, large-scale, deep construction is being carried out adjacent to subway tracks in korea. when excavating adjacent to each other, it is very important to ensure the safety of earth retaining structures and underground structures. therefore, we are managing the safety of the subway by introducing an automated measurement system. deformation of the subway track during adjacent excavation may affect train running stability. this is a factor that can be linked to train derailments. however, current subway track safety evaluation using automated measurement systems relies only on the maximum value of measured data. therefore, a method to improve the usability of automated measurement system results is needed. in this study, we utilized a technique that can quantitatively evaluate the measurement results of a large amount of subway track deformation. a safety evaluation was conducted on subway track deformation due to adjacent excavation using a vast amount of data using probabilistic statistical analysis techniques.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Environmental Policy
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• v.12 no.1
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• pp.37-58
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• 2013

In recently, the low impact development(LID) is discussed at various fields being related to urban stormwater, non-point source pollution, and quality of life. It is understood as an integrated development tool to induce sustainable development with various value-social, economic, and aesthetic. As concerning the development of waterfront area, the low impact development is interested in environmental planning. But the planning process and factors are not considered in precedent research. This study has two purposes. The one is to understand the planning process and factors of low impact development from literature review. The other is to apply the planning factors using case study and to know the effect of low impact development as the simulation plan. The simulation plan is based on some landuse planning. It is divided into the setting the region for environmental protection and the function of public facilities, spatial planning for enlarging permeable area, and spatial planning for circulation of water. The simulation model uses the LIDMOD2. The 14 planning factors of low impact development is applied to case region. And the effect is about 7~10 percent in reduction of nonpoint source pollution and surface runoff.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.112-114
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• 2003

긴박한 임무 상황(mission critical)의 시스템 뿐만 아니라, 웹 서버등의 고가용성, 고신뢰성을 위한 가상 서버의 구축도 관심이 되고 있다. 본 연구에서는 가상서버의 고가용성 , 고신뢰성을 보다 향상시키기 위해 가상서버 클러스터의 각 구성 노드들을 이중의 네트웍 채널로 중복 시켜 네트웍 고장에 대한 신뢰성을 향상시켰다. 관리자 노드와 작업노드 풀로 구성되는 시스템의 각 노드와 이중 채널로 구성된 네트웍에 대한 결함검출과 결함복구를 위한 분산 결함 허용 미들웨어를 구현하였고, 적응형 고장 감내 (Adaptive Fault Tolerance) 기법을 사용하여 다양한 임무 상황에서의 자원 효율성을 향상시켰다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.469-471
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• 2004

오늘날, 가용성과 중단 없는 서비스 제공에 대한 관심 고조, 개방화되는 시스템, 중앙 집중적 단일화된 관리와 통제 적용의 어려움으로 시스템이 주어진 임무를 수행해 나갈 수 있는 능력인 생존성이 요구되고 있다. 정보통신기반의 생존성을 평가하기 위해서는 평가 방법론을 정의해야하고 이를 뒷받침해주는 기술적인 연구가 수행되어야한다. 특히, 복잡 다양한 시스템들로 구성된 현재의 정보통신기반 네트워크의 특성과 실제 공격을 통해 테스트하기 어려운 현실을 감안하여, 시뮬레이션 기법을 사용한 생존성 평가기술이 연구되고 있다. 보안 시뮬레이션은 공격자, 네트워크 모델, 성능평가 모델, 원인.결과 모델들로 이루어져 있으며 이 중 원인.결과 모델이란 모델들 자체의 동적 정보이며 모델들간을 이어주는 관계 데이터이다. 본 논문에서는 원인-결과 모델링을 위해 단위 취약점(Atomic Vulnerability)을 이용한 취약점 분석 방법을 제안하고 이를 토대로 한 시뮬레이션용 취약성 데이터베이스인 VDBFS의 구축과정과 결과를 소개한다.

• Annual Conference of KIPS
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• 2010.11a
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• pp.1281-1284
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• 2010

오늘날 인터넷 기술의 발전과 대중화와 함께 다양한 악성코드가 빠르게 제작, 유포되고 있다. 최근 빠르게 증가하는 악성코드를 신속하게 대응하기 위해 자동화된 분석 기법에 대한 연구가 활발히 진행되고 있다. 자동화된 악성코드 분석 결과로 생성되는 데이터는 안티바이러스 업체나 관련 기관 등에서 알려지지 않은 악성코드에 대응할 수 있는 시그니처를 생성하는데 활용된다. 따라서 저장되는 악성코드 분석 결과는 악성코드 사이의 행위와 특성 관계가 고려되어 저장되어야 한다. 즉, 자동화된 악성코드 분석 결과를 효율적으로 저장할 수 있는 DB 설계가 필요하다. 본 논문에서는 악성코드의 대부분을 차지하는 PE를 대상으로 자동화된 악성코드 분석 결과를 효율적으로 저장할 수 있는 DB 설계를 제안하고자 한다.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.125-133
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• 2016

The Project Risk Management is intended to result in the effective management by identifying in advance and mitigating all significant risks including project risks and opportunities during the entire project life cycle - from project inception to completion of construction. It is impossible to predict an exact budget and construction duration before finishing a project. So, Washington Department of Transportation mandates that workshop-based risk management is conducted for projects over specific cost. However, the domestic construction sites have depended on numerical risk analysis without any workshop and efficient risk management have not made. Therefore, in this study, we propose the effective risk management using the RBES program which is very useful for workshop-based risk management and pre & post mitigation, by workshop-based risk management techniques. This proposed risk management approach is applied to a domestic 'A' river recovery project. It is concluded that we may expect the effect to mitigate the total cost overrun problem and the construction duration delay effect in the project by identifying significant risks and by preparing effective risk mitigation strategies.