• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1809-1827
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• 2013

Recently we need to take various measures to prepare for extreme flood that occur due to climate change. It is important that establish flood forecasting system to prepare flood over non-structure measures. The objective of this study is to develop superior real-time flood forecasting model by comparing the Neuro-fuzzy model and the multiple linear regression model. The Neuro-fuzzy model and the multiple linear regression model are established using same input data and applied for various flood events in Nakdong basin. The results show that the Neuro-fuzzy model can carry out flood forecasting results more accurately than the multiple linear regression model. This study can contribute to the establishment of a high accuracy flood information system that secure lead time in Nakdong basin.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.11-16
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• 2003

This Paper extracts the edge of main components of face with Gator wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.101-106
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• 1992

In this paper fuzzy goal programming problems with fuzzy constraints and fuzzy coefficients in both matrix and right hand side of the constraints set are considered. Because of fuzzy coefficients in both members of each constraint ranking methods for fuzzy numbers are considered. An additive model to solve fuzzy goal programming problems is formulated. The diversity of each methods provides a lot of different models of auxiliary linear programming problems from which fuzzy solutions to the fuzzy goal programming problem can be obtained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.20
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• pp.97-104
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• 1989

In this thesis, each objective functions with hierarchical system Bi-level linear programming (BLPP) Problem applications to fuzzy set theory conducted multiple objective programming problem. Using linear fuzzy membership functions make a change typical BLPP and presents modified method turn to account established BLPP method, presents operation results lead to example. Fuzzy Bi-level linear programming problem (FBLPP) can be natural describe realities of life then BLPP.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.489-494
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• 2015

The purpose of this paper is to analyse outcomes of Korean Pro-basketball games using regression models. Both Classic Fuzzy Regression Model and Fuzzy Regression Model applying linguistic variables were used to meet the purpose of the paper. In General Regression Analysis, in which the results of games are expressed and analyzed through score differences, a regression model is proposed considering influential variables for the score differences of the two teams. In Fuzzy Regression Analysis, the results are sorted into six different literal expressions, 'win with large margin, win with moderate margin, win with narrow margin, defeat with narrow margin, defeat with moderate margin, and defeat with large margin'. Athletic performances and team work of each teams were expressed in fuzzy number to analyse how much athletic performances and team work affect results of games. This paper referred back to 2013-2014 season data provided by KBL(Korean Basketball League) and professional columns on Korean basketball analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.128-132
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• 2012

물은 생물의 생존을 위해 필수적인 요소로 인류가 시작된 이래로 물을 효율적으로 이용하고 안전하게 관리하기 위한 노력은 계속되어 왔다. 최근 지구 온난화가 주요 원인으로 알려진 국지성 집중호우의 피해는 매우 심각하며, 이로 인해 치수에 대한 중요성은 날로 커지고 있다. 지금까지 사용해 왔던 홍수 예 경보 과정은 특정 지점의 유출량을 예측하기 위해서 강우-유출 모형을 운영하였다. 그러나 물리적 모형의 경우 운영에 필요한 매개변수의 결정과정이 복잡하고, 매개변수 결정을 위해 많은 자료를 필요로 한다. 또한 그 매개변수의 결정과정은 많은 불확실성을 포함하고 있어서 모형의 운영을 위한 전처리과정과 계산과정을 거치는 동안 발생한 오차가 누적되어 결과물 속에는 많은 오차가 포함되어 있다. 본 연구에서는 기존의 홍수 예 경보 시스템의 문제점과 불확실성을 최대한 감소시키고 더 우수한 유출량 예측을 위해 neuro-fuzzy 추론 기법을 이용한 모형인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 사용하여 하천수위를 예측하였다. ANFIS는 신경회로망과 퍼지이론을 결합한 기법으로 신경회로망의 구조와 학습 능력을 이용하여 제어환경에서 획득한 입 출력 정보로부터 언어변수의 membership 함수와 제어규칙을 제어 대상에 적합하도록 자동으로 조종하는 기법이다. 본 연구에서는 ANFIS를 사용하여 탄천 하류에 위치한 대곡교의 수위를 예측하였다. 분석을 위해 2007년부터 2011년까지의 탄천 유역의 관측 강우자료와 수위 자료 중 강우강도와 지속시간, 강우 형태에 따라 7개의 강우사상을 선정하였다. 학습자료 및 보정자료의 변화에 따른 예측 오차를 비교하여 모형의 적용성과 적정성을 평가하였다. 적용결과 입력자료 구성의 경우 해당 시간의 강우량 및 수위자료와 10분 전 강우자료를 이용한 모델이 가장 우수한 예측을 보였고, 학습자료의 경우 자료의 길이가 길고, 최대홍수량이 큰 경우 가장 우수한 예측 결과를 보였다. 본 연구의 적용결과 가장 우수한 모형의 경우 30분 예측 첨두수위 오차는 0.32%, RMSE는 0.05m 이고 예측시간이 길어짐에 따라 오차가 비선형적으로 증가하는 경향을 보였다.

• Computational Structural Engineering
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• v.4 no.3
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• pp.107-115
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• 1991

A probabilistic-Fuzzy model for seismic hazard analysis is developed. The proposed model is able to reproduce both the randomness and the imprecision in conjunction with earthquake occurrences. Results-of this research are (a) membership functions of both peak ground accelerations associated with a given probability of exceedance and probabilities of exceedance associated with a given peak ground acceleration, and (b) characteristic values of membership functions at each location of interest. The proposed probabilistic-fuzzy model for assessment of seismic hazard is successfully applied to the Wasatch Front Range in Utah in order to obtain the seismic maps for different annual probabilities of exceedance, different peak ground accelerations, and different time periods.

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.