• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 춘계학술대회 논문집
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• pp.434-436
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. The percentage errors of 24 hourly load forecasting for holidays is relatively large. In this paper, we propose the maximum and minimum load forecasting method for holidays using a fuzz linear regression algorithm. 24 hourly loads are forecasted from the maximum and minimum loads and the 24 hourly normalized values. The proposed algorithm is tested for 24 hourly load forecasting in 1996. The test results show the proposed algorithm improves the accuracy of the load forecasting.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.471-473
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• 2010

본 논문은 선형회귀(LR: Linear Regression)와 국부적인 방사기저함수 네트워크(RBFN: Radial Basis Function Networks)를 결합한 점진적인 모델(incremental model)의 설계와 관련되어진다. 전형적인 RBFN에 의한 모델링과는 달리, 제안된 방법의 근본적인 원리는 두 단계에 의해 고려되어진다. 첫째, 전체 모델의 설계과정에서 전역적인 모델로써 선형회귀에 의해 데이터의 선형부분을 구축한다. 다음으로, 모델링 오차는 오차가 존재하는 국부적인 공간에서 RBFN에 의해 보상되어진다. 여기서, 오차의 분포로부터 RBFN을 설계하기 위해 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)를 통해 정보입자의 형태로 구축되어진다. 실험은 자동차 mpg 연료소비량 예측과 부동산 가격예측문제를 통해 제안된 방법의 우수성을 증명한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.264-267
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• 1998

We are dealing with the preliminary diagnosis from the information of headache interview chart. We quantify the qualitative information based on the interview chart by dual scaling. Prototype of fuzzy diagnostic sets and the neural linear regression methods are established with these quantified data, These new methods can be used to classify new patient's tone of diseases with certain degrees of belief and its concerned symptoms. We call these procedures as neural Fuzzy Differential Diagnosis of Headache (NFDDH-1). Also we investigate three measures to medical diagnosis, where relations between symptoms and diseases are described by intutionistic fuzzy set (IFS) data. Two measures are described by nin-max and max-min IFS operators, respectively. Another measure is the similarity degree, i.e., IFS distance between patient's symptoms and prototypes of diseases. We consider some reasonable criteria for three measures in order to determine the label of headache, We will establish hree measures in NFDDH-2 and combine two packages as NFDDH

• 지능정보연구
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• 제8권1호
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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.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• International Journal of Aeronautical and Space Sciences
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• 제9권2호
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.379-384
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• 1994

In this paper, a fish-drying control method is proposed, which utilizes prediction of proper change in- weight of material fish based on skilled worker's performance. The function of the proposed system is largely broken down into two procedures: The procedure before drying and the one during drying. The procedure before drying is the determination of necessary drying conditions and the required drying time. Required drying time and proper changes in weight for a specific product are obtained by using fuzzy inference and regression models. The procedure during drying is the prediction of the state of material at the end of drying, or the state of product and regulation of drying conditions to attain the prescribed goal before drying. The prediction of product is obtained by using a set of linear-differential equations obtained by the authors' previous work. Drying conditions are regulated by using fuzzy inference. A good agreement between the results of simulation and experiments is obtained, which implies the usefulness of the present control method.