• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.859-866
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• 1994

It is difficult to make an exact estimate of the peak discharge or the runoff depth of flood and establish the proper measure for the flood protection since the water stage or discharge has been nearly measured at most medium or small river basins. The objective of this study is to estimate parameters of the discrete, linear, input-output model for medium or small river basin. The On-Cheon River basin in Pusan was selected for the study area. The runoff data used in the study has been observed since June 1993, and the effective rainfall was determined using the storage function method. The parameter sets of the discrete, linear, input-output model were estimated using the least squares method and the correlation function method, respectively. The calculated hydrographs by the discrete, linear, input-output model regenerated the observed outflow hydrographs well, and also the simulated flood hydrograph was comparable to the observed one. Therefore, it is believed that the discrete, linear, input-output model is simpler than other runoff analysis methods, and can be applied to a medium or small river basin.

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

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.792-797
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• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 1993.10a
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• pp.25-25
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• 1993

• ICROS
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• v.15 no.3
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• pp.47-51
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• 2009

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.368-370
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• 1999

• Proceedings of the Korea Water Resources Association Conference
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• 1993.07a
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• pp.393-400
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• 1993

• ICROS
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• v.15 no.2
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• pp.26-30
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• 2009

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1937_1938
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• 2009

이 논문에서는 1차원 혼돈맵들 중의 하나인 톱니맵을 8비트의 유한정밀도로 이산화시켜 설계하였고, 이 이산화된 톱니맵을 사용한 혼돈 2진 순서 발생기의 회로도도 제시하였다. 설계된 혼돈맵의 실제 구현은 이산화된 진리표로부터 얻어진 출력변수의 간략화된 부울함수에 따른 입력선과 출력선들의 정확한 연결만에 의해 실현하였다. 최대길이를 발생시키는 선형궤환시프트레지스터(mLFSR)에 의해 발생되는 난수성 2진 출력 순서들을 이산화된 톱니맵의 입력순서로 사용함으로써 결과적으로 최소 8배 더 긴 주기를 갖는 혼돈 2진 순서들을 발생시켰다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.04a
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• pp.246-251
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• 1995

유연구조물의 진동제어를 위해 압전필름 작동기를 사용하였고 이와 연계하 여 샘플링 과정을 고려한 DSMC를 구성하였다. 변수변화 및 외란의 경계치 를 알고 있다는 가정하에 시스템을 이산화시킨 후 제어기를 설계하였다. 일 반적으로 연속시간 시스템의 이산화 과정에서 정합조건의 가정이 보장되지 않기 때문에 제어기 설계시 이를 배제하였다. 이산시간 리아푸노프 이론을 근거로 DSMC의 존재성을 도출하였고, 이것에 기초하여 슬라이딩 모드가 발생되도록 불연속제어기를 설계하였다. 이때, 불연속게인을 상수로 하지 않고 RP의 위치에 따라 계속 새롭게 수정함으로써 슬라이딩 영역을 최소화 하였다. 그리고 기존의 DSMC 기법상에서 발생되는 슬라이딩 영역의 문제를 .betha. 등가제어기를 이용하는 이원화 방법으로 극복하였으며, 컴퓨터 시뮬 레이션을 통하여 제안된 기법의 설계 효율성 및 제어기의 강건성을 입증하였다. 향후, 제안된 기법에 대한 실험적 고찰 및 중요 제어인자에 대한 연구가 계속적으로 수행될 예정이다.