• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.256-258
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• 2001

전력 계통의 운용 계획을 최적화 하기 위해서 수요예측에 관한 연구가 활발히 진행되고 있다. 기존의 수요예측 기법의 최대 오차는 특수일이 토요일과 월요일인 경우와 연휴인 경우에 발생한다. 이 중 특수일이 토요일과 월요일인 경우는 퍼지 선형회귀분석법과 상대계수법을 이용하여 우수한 결과를 도출한 바 있다. 구정과 추석은 특수일 중 평일과의 부하 차이가 가장 큰 특수일이며 약 $45{\sim}50%$ 정도가 감소된다. 이러한 부하의 감소 폭은 서서히 줄어서 연휴 당일 4일 후에는 완전히 복구가 되며 연휴 전 부하가 낮아지는 시점은 연휴 당일 3일 전이다. 연휴 예측의 불확실성은 연휴 기간의 길이 변동 및 기타 다양한 변수들에 의한 유동성에 기인한다. 특히 추석의 경우 과거 데이터 이용에 더욱 신중해야 하며 타 특수일에 비해 부하 값의 예측이 힘들다. 또한 직전 평일 대비 추석 연휴의 부하는 변화가 심하게 나타나며 본 논문에서는 퍼지 선형회귀분석법을 기본으로 변형된 알고리즘으로 향상된 예측도를 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.24-26
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• 1999

본 논문에서는 용해로 이상감시를 위한 실시간 유리 용해로 운전 전문가시스템을 구축한 결과를 소개한다. 유리 용해 공정에서는 운전자의 경험지식에 의해 내부의 상황을 판단하게 되고, 이는 용해로 수명과 제품의 품질에 중요한 영향을 준다. 이를 전문가 시스템으로 구현하기 위하여, 먼저, 기존 운전자의 지식을 취합, 분석한다. 그 후, 취합된 각 지식들의 특성에 부합하도록 이진 룰(Crisp Rule)과 퍼지 룰(Fuzzy Rule)로 구분한다. 이 때, 선형 회귀분석을 통하여 퍼지 룰의 입력을 결정함으로써 보다 정확한 운전 지식의 표현이 가능하도록 하였다. 설계된 알고리즘은 젠심 (Gensym)사의 실시간 전문가 시스템 개발 툴인 G2를 사용하여 구현하였다. 제시된 퍼지 전문가 시스템은 삼성코닝(주) 수원사어장의 실제생산 용해 공정에 직접 적용하여 그 효율성이 검증되었다.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.65-65
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• 2002

본 논문에서는 용해로 이상감시를 위한 실시간 유리 용해로 운전 전문가시스템을 구축한 결과를 소개한다. 유리 용해 공정에서는 운전자의 경험지식에 의해 내부의 상황을 판단하게 되고, 이는 용해로 수명과 제품의 품질에 중요한 영향을 준다. 이를 전문가 시스템으로 구현하기 위하여, 먼저 기존 운전자의 지식을 취합, 분석한다. 그 후,취합된 각 지식들의 특성에 부합하도록 이진 규칙(Crisp Rule)과 퍼지 규칙(Fuzzy Rule)으로 구분한다. 이 때, 선형 회귀분석을 통하여 퍼지 규칙의 입력을 결정함으로써 보다 정확한 운전 지식의 표현이 가능하도록 하였다. 설계된 알고리듬은 젠심(Gensym)사의 실시간 전문가 시스템 개발 툴인 G2를 사용하여 구현하였다. 제시된 퍼지 전문가 시스템은 삼성코닝(주) 수원사업장의 실제 생산 용해 공정에 직접 적용하여 그 효율성이 검증되었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.211-216
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• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture o fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so 솜 t the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1539-1540
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• 2008

운전자의 경험과 판단에 전적으로 의존하는 빙축열 시스템의 기존 운전방식에서는 운전자의 그릇된 판단과 미숙한 운전으로 인해 과잉 축열이나 냉방공급량 부족현상이 자주 초래된다. 본 논문에서는 경제적이고 효율적인 빙축열 시스템의 운용을 위해 다음날의 구간별 온도, 습도와 냉방부하를 예측하는 자기구성퍼지모델 구축방안을 제안한다. 제안된 방법의 성능과 실제 적용가능성을 검증학기 위하여 한국전력 속초 생활연수원을 대상으로 제안된 방법과 신경회로망, 퍼지모델, 선형회귀모델 등을 이용한 기존의 방법을 적용하여 구한 냉방부하, 온도, 습도의 예측정확도를 비교 분석한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Korean Journal of Social Welfare
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• v.64 no.2
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• pp.31-54
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• 2012

Along with increasing mothers' employment, work-family reconciliation has been recognised as a key policy agenda in contemporary welfare states. Although various policy instruments have been introduced and expanded in recent years, the problem of time allocation within couples still remains as a fundamental issue, which has been largely underresearched at a micro perspective. In this context, this study aims to identify dominant types of work-family time allocation within married couple, and to apply these types to the Korean case using the fuzzy-set ideal type analysis. Further, a series of multiple regression analyses will be implemented to find factors affecting each ideal type of work-family time allocation. The 1999 and 2009 Korea Time Use Survey datasets will be adopted for the analyses. Married couples are selected as samples only when men work 40 hours or more per week and they have at least one pre-school child. Empirical analyses cover three parts. First of all, four ideal types on work-family time allocation are classified by intersecting two core variables - the ratio of men's (paid) working and family (caring time plus domestic work) time to total working and family time. In this research, the four types will be labelled the traditional male breadwinner model (TM, high working and low family time), the dual burden model (DB, shared working but low family time), the family-friendly male breadwinner model (FM, high working but shared family time), and the adaptive partnership model (AP, shared working and shared family time). By comparing the composition of the four ideal types in 1999 and 2009, it will examine the trend of work-family time allocation in Korea. In addition, multiple regressions will be useful for investigating which characteristics contribute to the different degree of each fuzzy ideal score in the four models. Finally, policy implications and further research agenda will be discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.233-239
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• 2006

Most research for until at now link travel time were research for mean link travel time calculate or estimate which uses the average of the individual vehicle. however, the link travel time distribution is divided caused by with the impact factor which is various traffic condition, signal operation condition and the road conditional etc. preceding study result for link travel time distribution characteristic showed that the patterns of going through traffic were divided up to 2 in the link travel times. therefore, it will be more accurate to divide up the link travel time into the one involving delay and the other without delay, rather than using the average link travel time in terms of assessing the traffic situation. this study is it analyzed transit hour distribution characteristic and a cause using examine to the variables which give an effect at link travel time distribute using simulation program and determinate link travel time distribute ratio estimation model. to assess the distribution of the link travel times, this research develops the regression model and the fuzzy model. the variables that have high level of correlations in both estimation models are the rest time of green ball and the delay vehicles. these variables were used to construct the methods in the estimation models. The comparison of the two estimation models-fuzzy and regression model- showed that fuzzy model out-competed the regression model in terms of reliability and applicability.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.63-74
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• 2002

This paper presents an application result of on-line fault diagnosis system for glass melting furnace using a fuzzy expert system. Operators maintain the furnace using the furnace Knowledge and experience, which directly influence the furnace and glass product. Firstly, knowledge and experience is achieved and analyzed to implement the furnace Knowledge and experience into fuzzy expert system. The acquired Knowledges determined as a crisp rule or a fuzzy rule to expect its characteristics. And, a linear regression is used as the input of fuzzy rule to consider the exact knowledge of human operator. The fuzzy expert system is implemented with G2 which is an on-line expert system tool of Gensym Co. The application to a production furnace of Samsung-Corning Co. in Suwon shows successful results of proposed fuzzy expert system.