• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.3-6
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• 2001

This paper presents optimal robust PID controller design method for second order systems to satisfy the design specifications in time domain. The parameters of PID controller are determinated by the weighting factors Q and R of cost function. It is suggested that the selection of Q and R matrix can be determinated by its relationship with the natural frequency of ITAE criterion.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.597-602
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• 1987

In this paper, an adaptive observe based upon the exponentially weighted least-squares method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. A method of selecting the state variable filter is proposed. In this scheme, all the past data are weithted exponentially with the weighting coefficient.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2003.05a
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• pp.181-182
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• 2003

생물에게 물리적인 손상(physical damage)이나 stress를 주지 않기 위한 마취제로서의 역할은 매우 중요하다. 특히 전복은 어류와는 달리 부착기질과 은신처를 이용하여 부착생활을 하기 때문에 무게측정(weighting), 표지(tagging), 선별(selection), 밀도 조절 등을 위해서 박리작업은 반드시 필요하다. 따라서 본 연구는 요소비료(CO(NH$_2$)$_2$)를 마취제로 이용하여 요소비료의 농도와 수온 등 물리적 요인이 시볼트 전복 Haliotis gigantea의 박리율과 회복율에 미치는 영향을 조사함으로서 경제적이며 효과적인 박리 기술을 개발하고자 하였다. (중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.199-202
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• 2000

본 논문에서는 퍼지-유전자 접근방법을 이용한 무감독 특징 선택방법에 대하여 나타내었다. 이 방법은 각각의 특징들의 중요도에 따라 순서를 정하기 위해 사용되는 weighted distance 를 포함하는 특징 평가 지표 (feature evaluation index)를 최소화시키는데 있다. 또한 특징 평가 지표에서 사용되는 각 패턴들의 쌍에 대하여 근접함의 정도를 퍼지 멤버쉽 함수를 이용하여 결정하고 유전자 알고리즘은 평가 지표를 최소화시킴으로써 각 특징의 중요도를 나타내는 최적의 weighting 계수의 집합을 한기 위하여 적용하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.429-430
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• 2019

본 논문에서는 최적화 기법에 기반한 지능형 시스템의 재무응용사례를 다룬다. 본 연구에서 제안하는 모형은 대표적인 최적화 기법 중 하나인 시뮬레이티드 어니일링인데 이는 유전자 알고리듬과 유사한 최적화 성능을 가지고 있는 것으로 알려져 있으나 재무분야에서 응용된 사례가 거의 없다. 본 연구에서 제안하는 지능형 시스템은 시뮬레이티드 어니일링과 기계학습 기법을 결합한 것이다. 일반적으로 최적화와 기계학습 기법을 결합하는 방법은 특징선택(feature selection), 특징 가중치 최적화(feature weighting), 사례선택(instance selection), 모수 최적화(parameter optimization) 등의 방법이 있는데 선행연구에서 가장 많이 사용된 것은 특징선택에 두 기법을 결합하는 방식이다. 본 연구에서도 기계학습 기법을 재무 문제에 활용함에 있어서 최적의 특징선택을 위해 시뮬레이티드 어니일링을 결합하는 방식을 사용한다. 본 연구에서 제안된 기법의 유용성을 확인하기 위하여 실제 재무분야의 데이터를 활용하여 예측 정확도를 확인하였으며 그 결과를 통하여 제안하는 모형의 유용성을 확인할 수 있었다.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.629-640
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• 2010

The exemplar-based inpainting model is widely used to remove objects from natural images and to restore a damaged region. This paper presents a method which improves the performance of the conventional exemplar-based inpainting model by modifying three major parts in the model: data term, confidence term and patch selection. While the conventional data term is calculated using the local gradient, the proposed method uses 16 compass masks to get the global gradient to make the method robust to noise. To overcome the problem that the confidence term gets negligible in the inside of the eliminated region, a method is proposed which makes the confidence term decrease slowly in the eliminated region. The patch selection procedure is modified so that the closer patch has higher weight. Experiments showed that the proposed method produced more natural images and lower reconstruction error than the conventional exemplar-based inpainting.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.205-224
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• 2012

One of the limitations of BOW method is that each term is recognized only by its form, failing to represent the term's meaning or thematic background. To overcome the limitation, different profiles for each term were defined by thematic categories depending on contextual characteristics. In this study, a specific term was used as a classification feature based on its meaning or thematic background through the process of comparing the context in those profiles with the occurrences in an actual document. The experiment was conducted in three phases; term weighting, ensemble classifier implementation, and feature selection. The classification performance was enhanced in all the phases with the ensemble classifier showing the highest performance score. Also, the outcome showed that the proposed method was effective in reducing the performance bias caused by the total number of learning documents.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Journal of KSNVE
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• v.10 no.2
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• pp.247-253
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• 2000

The evaluation of the ride quality had been performed by the subjective method before ISO2631(International Organization for Stadard 2631) and BS6841(British Standard 6841) was precented, but many research programs have been performed by the objective method after that. On this study, the ride quality was evaluated related with the objective method which considered the vibration which the human body feels on the driver's seat while driving on the road. In particular, we made the shock absorber nonlinear model and also selected the suitable shock absorber in the part of the vibration which the human body feels into the simulation. The shock absorber of suspension was dealt with 3 cases respectively with the front wheel and rear wheel. The vibration of the car driving on the road can be transferred to the wheel, the suspension, the vehicle body, the seat and the human body. The signal which was gained from the seat(hip) and the floor(foot) of the human body was changed to the vibration signal which the human body felt through using the frequency weighting function. And then the performance of the shock absorber was calculated through the statistic processing.