• Journal of computational fluids engineering
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• v.9 no.3
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• pp.26-38
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• 2004

A study has been made for the investigation of the robustness and convergence of various implicit operators of the LU-SGS scheme using linear stability analysis. It is shown that the behavior of the implicit operator is not determined by its own characteristics, but is determined relatively depending on the dissipative property of the explicit operator. It is also shown that, as the dissipation level of the implicit operator increases, the robustness of the scheme increases, but the convergence rate can be deteriorated due to the excessive dissipation. The numerical results demonstrate that the dissipation level of the impliict operator needs to be higher than that of the explicit operator for computing stiff problems.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.73-77
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• 2004

In the present study, an efficient and robust implicit operator for the LU-SGS method is proposed. Numerical experiments for supersonic flow are performed to demonstrate the performance of the proposed method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.9
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• pp.34-40
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• 2005

In present study, a two dimensional unsteady Navier-Stokes solver has been developed using the Diagonalized ADI (DADI) method and implicit dual time stepping method. The jacobian matrices in steady state Navier-Stokes equations are introduced from inviscid flux terms. The implicit treatment of artificial dissipation terms results in a block penta-diagonal matrix system and it becomes a scalar penta-diagonal matrix by diagonalization. In steady state equations about fictitious time, a new residual including a real time derivative term is introduced. From a converged solution about fictitious time, a real time unsteady solution can be obtained, which is called 'implicit dual time stepping method'. For code validation, an oscillating flat plate, a regular Karman vortices past a circular cylinder and shock buffeting around a bicircular airfoil problems are numerically solved. And they are compared with a theoretical solution, experiments and other researcher's computations.

• Journal of computational fluids engineering
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• v.9 no.3
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• pp.39-48
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• 2004

In the present study, an efficient implementation technique of the van Leer's implicit operator is suggested in accordance with the Roe's explicit operator. By using an efficient treatment of the off-diagonal terms, which occupy most of the memory requirement for the linear system of equations, it is shown that the improved scheme only requires less than 30% of memory and is approximately 10-20% faster than the baseline scheme.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.1-9
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• 2004

This study concentrates on the upwind schemes for convergence acceleration of the multigrid method for the Navier-Stokes equations. Comparative study of the upwind schemes in the Fourier space has been performed to identify why the second-order upwind scheme with enlarged stencil can be preconditioned better than the classical second-order upwind scheme. The full-coarsening multigrid method with implicit preconditioned multistage scheme has been implemented for verification of analysis. Numerical simulations on the inviscid and turbulent flows with the Spalart-Allmaras turbulent model have been performed. The results showed consistent trend with the analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.1709-1712
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• 2002

지식 탐사 연구의 핵심이 되어온 데이터 마이닝은 축적 데이터로부터 쉽게 추출되지 않는 데이터 상호관계나 일정 패턴과 같은 유용한 내재 정보 추출을 주된 목적으로 수행된다. 그러나, 데이터 마이닝은 대용량의 데이터 처리로 인해 빈번한 메모리 공간 제약과 처리 속도 저하 등의 한계성을 드러낸다. 이를 극복하기 위해 많은 마이닝 알고리즘 개발과 기존 알고리즘 개선 방법이 제시되어 왔으나 여전히 궁극적인 해결방안은 대두되지 않고 있다. 따라서, 만약 데이터 전처리 과정을 통해 마이닝 목적에 적합한 부분 데이터집합 추출 및 가공이 선행된다면 보다 효율적인 데이터 마이닝 작업을 유도할 수 있을 것이다. 본 논문은 효과적 데이터 전처리를 위한 필수 기본 연산 기능들을 주어진 데이터집합의 트랜잭션 및 데이터 특성에 기초하여 관계형 대수 형태로 의미를 정립하고, 적용 사례에 의한 상세 설명 및 실제 구현된 온라인 데이터 전처리 시스템을 제안한다.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.301-314
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• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.217-228
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• 2009

Data analysis applications typically aggregate data across many dimensions looking for unusual patterns in data. Even though such applications are usually possible with standard structured query language (SQL) queries, the queries may become very complex. A complex query may result in many scans of the base table, leading to poor performance. Because online analytical processing (OLAP) queries are usually complex, it is desired to define a new operator for aggregation, called the data cube or simply cube. Data cube supports OLAP tasks like aggregation and sub-totals. Many aggregate functions can be used to construct a data cube. Those functions can be classified into three categories, the distributive, the algebraic, and the holistic. It has been thought that the distributive functions such as SUM, COUNT, MAX, and MIN can be used to construct a data cube, and also the algebraic function such as AVG can be used if the function is replaced to an intermediate function. It is believed that even though AVG is not distributive, but the intermediate function (SUM, COUNT) is distributive, and AVG can certainly be computed from (SUM, COUNT). In this paper, however, it is found that the intermediate function (SUM COUNT) cannot be applied to OLAP cubes, and consequently the function leads to erroneous conclusions and decisions. The objective of this study is to identify some problems in applying aggregate function AVG to OLAP cubes, and to design a process for solving these problems.