• Journal of the Society of Naval Architects of Korea
• /
• v.34 no.2
• /
• pp.39-47
• /
• 1997

In the present paper, viscous flow fields around a wigley hull with appendages are analysed to study interactions between the hull and appendages. Navier-Stokes and continuity equations are solved by a finite volume method in a body-fitted coordinate system which conforms three dimensional ship geometries with appendages. A Sub-Grid Scale(SGS) turbulent model is used for a calculation of high Reynolds number flow. Numerical computations has been done for a Wigley hull form at $Rn=1.0{\times}10^6$. The results show that the present approach can predict, at least in qualitative sense, the influence of the appendages upon the flow field around a ship.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.264-264
• /
• 2019

개수로 흐름 해석을 위해 수치모형을 적용할 때 반드시 거쳐야 하는 과정이 격자망을 구성하는 일이다. 불규칙한 형상의 자연수로를 모의할 때 격자망 생성은 쉬운 일이 아니며, 따라서 가시적으로 격자망 생성을 돕고, 격자망의 수정도 용이한 도구가 요구된다. 따라서 본 연구에서는 수심적분된 흐름방정식을 지배방정식으로 하여 개수로 흐름 해석을 용이하게 하고자 그래픽 사용자 인터페이스(GUI)를 개발하였으며, 이를 소개하고자 한다. 격자망은 기본적으로 사각형과 삼각형 요소로 구성될 수 있으며, 유한차분모형 등에서는 정형사각형 격자망을, 유한요소모형에서는 비정형 사각형 및 삼각형 격자망 또는 혼합망을 생성시킬 수 있다. 이산점(scatter points)이나 절점(node points)을 생성하거나 기존의 자료를 불러들여 삼각망 또는 사각망을 형성시킬 수 있으며, 연속선(polylines)을 작성하여 형성된 폐다각형(polygones)을 이용하여 정규 또는 비정규의 삼각망 또는 사각망을 형성시킬 수 있다. 또한 두 점사이를 선형 또는 반원 형태로 편향 정도(biased value)를 설정하여 원하는 개수만큼 나눌 수 있도록 하여, 보다 효율적인 격자형성이 가능토록 하였다. 기존 상용 프로그램에서 작성된 격자를 불러들여 활용 가능하며, 백그라운드 이미지로 지형도나 위성사진을 띄어놓고 이미지 상에서 격자를 형성할 수도 있다. 기본적으로 마우스를 이용하여 화면의 이동, 확대 및 축소와 점, 선, 요소의 생성 및 선택이 가능하다. 본 프로그램은 Qt와 modern OpenGL을 바탕으로 제작되었으며, 마이크로소프트사의 windows 뿐만 아니라 Mac OS, Linux 버전의 설치 파일 작성이 가능하다.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.