• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.349-353
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• 2006

국내에서도 최근 이상기후 현상이 빈번하게 발생하고 있고, 이로 인해 매년 봄가뭄과 여름홍수가 반복적으로 발생하여 효율적 수자원 관리의 중요성이 더욱 강조되고 있다. 최적 저수지 운영을 통한 효율적인 수자원 이용으로 과다한 무효 방류와 같이 낭비되는 수자원을 절감시켜 신규 수자원 개발과 유사한 효과를 획득하고, 기존 시설에 의한 지역 용수의 안정적인 공급으로 신규 수자원 개발 억제에 의한 비용 절감의 필요성과 유역의 수자원 변화를 평가하기 위한 모형의 개발이 필요하다. 본 연구에서는 저수량 확보, 생활.농업.공업.하천유지용수 공급, 홍수조절, 수력발전 등의 다양한 목적들을 적절히 고려하고, 사용자의 요구에 따라 목적별 우선권을 변경할 수 있도록 적절한 membership 함수를 구축하여 fuzzy DP 모형을 개발하였다. 또한, 개발된 fuzzy DP 모형에 소양강 다목적댐의 기왕의 수문자료를 도입한 모형의 최적화 운영결과와 기왕의 실적자료를 비교 검토하여 최적화 운영의 우수성을 확인하였다. 본 연구의 결과는 향후 저수지의 효율적인 운영을 위한 지침으로 사용될 수 있을 것이며, 유역의 수자원 영향 평가에 활용할 수 있을 것으로 기대된다.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.3 s.14
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• pp.61-72
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• 2004

In this research, multi reservoir operation methods for reservoirs in Han River are proposed based on the single dam operation models using fuzzy control techniques. The result of fuzzy controled single dam operation has shown that it can improve flood controllability at the downstream of dams. Among the many control rules of fuzzy operation, a rule that shows the most effective flood control rate at the downstream has been selected as Ike operation rule. The simulated results for 1990 and 1995 flood events are compared with historical ones. As the results, it is founded that suggested models can reduce the inundation of upstream and keep the water elevation lower at downstream, which make the proposed models as the effective methods in multi reservoir operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.99-105
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• 1998

This paper is to propose the fuzzy regression model using genetic algorithm which is fuzzy nonlinear regression model. Genetic algorithm is used to classify the input data for better fuzzy regression analysis. From this partition. each data can be have the grade of membership function which is belonged to a divided data group. The data group, from optimal partition of the region of each variable, have different fuzzy parameters of fuzzy linear regression model one another. We compound the fuzzy output of each data group so as to obtain the final fuzzy number for a data. We show the efficiency of this method by means of demonstration of a case study.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.781-790
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• 2009

This paper deals with a rank transformation method and a Theil's method based on an ${\alpha}$-level set of a fuzzy number to construct a fuzzy linear regression model. The rank transformation method is a simple procedure where the data are merely replaced with their corresponding ranks, and the Theil's method uses the median of all estimates of the parameter calculated from selected pairs of observations. We also consider two numerical examples to evaluate effectiveness of the fuzzy regression model using the proposed method and of another fuzzy regression model using the least square method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.366-370
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• 2013

Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper introduce Theil's method to find a fuzzy regression model which explain the relationship between explanatory variable and response variables. Theil's method is a robust method which is not sensive to outliers. Theil's method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. We propose an example to show Theil's estimator is robust than the Least squares estimator.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.10a
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• pp.203-204
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• 1999

대기질의 통계예측모형은 주로 오존 농도 예측에 사용된다. 통계예측 방법은 중회귀 모형, 신경망 모형, Fuzzy 논리 모형 등이 있다. 중회귀 모형은 종래 통계분석 방법으로 예전부터 많이 사용되고 있는 방법인 반면에 신경망 모형과 Fuzzy 논리 모형은 최근에 개발되어 적용가능성을 검토 중인 방법이다. 국내외 연구결과에 의하면 각 방법에 의한 고농도 오존 예측성은 크게 다르지 않았다. 국내에서는 중회귀 모형과 신경망 모형이 적용되었는데, 상관계수는 0.6-0.7저도로 보고되었다.(중략)

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.73-77
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• 2004

종속변수와 독립변수 사이의 통계적인 관계를 설명하기 위해 사용되는 회귀모형을 분석하는 방법을 회귀분석이라 한다. 독립변수와 종속변수가 퍼지수인 퍼지회귀모형을 추정하기 위해 최소전대편차추정량을 제시하고. 예제를 이용하여 퍼지최소절대편차회귀모형과 퍼지최소자 승회귀모형의 효율성을 평가한다.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.25-36
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• 2005

In this study, we suggested a new approach method forecasting distribution demand of urban rail transit usign fuzzy control, with intend to reflect irregularity and various functional relationship between trip length and distribution demand. To establish fuzzy control model and test this model, the actual trip volume(production, attraction and distribution volume) and trip length (space distance between a departure and arrival station) of Daegu subway line 1 were used. Firstly, usign these data we established a fuzzy control model, nd the estimation accuracy of the model was examined and compared with that of generalized gravity model. The results showed that the fuzzy control model was superior to gravity model in accuracy of estimation. Therefore, wwe found that fuzzy control was able to be applied as a effective method to predict the distribution demand of urban rail transit. Finally, to increase the estimation precision of the model, we expect studies that define membership functions and set up fuzzy rules organized with neural networks.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.57-67
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• 2000

Two fuzzy travel time estimators for interrupted traffic flow were developed based on field survey data and simulation data 7hat is collected from DETSIM, which is microscopic traffic simulation model that car-following theory is applied. One is FETTOS(Fuzzy Estimator of Travel Time using Occupancy and Spot speed) and the other is FETTOS(Fuzzy Estimator of Travel Speed using Volume and Occupancy). Fuzzy logic controller was applied to the estimators to deal with non-linear relationship between traffic variables and travel time. According to results of simulation and field survey. estimation of travel time can be modeled by using percent occupancy better than any other traffic variables. Detector location from storyline and signal timing Plan of intersection are affected to estimate travel time. With a few findings, the estimator was constructed and its performance was tested for observed travel time data and simulated data. FETTOS which needs signal timing plan and detector location estimates travel time with accurate better than FETSVO does. However. FETSVO has excellent transferability because the estimator needs set of input data only; volume and time mean speed.