• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.288-305
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• 2007

본 논문은 데이터 마이닝의 기법 중 가장 잘 알려진 지도학습 기법의 성능 향상을 위한 새로운 Hybrid 및 Combined 기법인 불일치 패턴 모델(오차 패턴 모델)에 대한 연구 논문이다. 불일치 패턴 모델이란 2개 이상의 기법 중 향후 더 레코드별로 더 잘 맞출 수 있는 기법을 메타 분류하는 불일치 패턴 모델을 개발하여, 최종적으로는 기존의 기법보다 더 좋은 분류 정확도 및 예측 향상율을 기대하기 위한 기법을 의미한다. 본 논문에서는 의사 결정나무 추론 기법인 C5.0과 C&RT 그리고 신경망 분석, 그리고 로지스틱 회귀분석과 같은 대표적인 데이터 마이닝의 지도학습 기법을 이용하여 불일치 패턴 모델을 생성하여 보고, 이들이 기존 단일 기법과 기존의 Combined 모델인 Bagging, Boosting 그리고 Stacking 기법보다 성능이 우수함을 23개의 실제 데이터 및 공신력 있는 공개 데이터를 이용하여 증명하여 보였다. 또한 데이터의 특성에 따라서 불일치 패턴 모델의 성능의 변화 및 더 우수해 지는지를 알아보기 위한 연구포 같이 수행을 하여 본 모델의 활용성을 높이고자 하였다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.306-310
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• 2007

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.130-136
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• 2001

본 논문에서는 새로운 학습알고리즘의 비선형 주요성분분석 신경망을 이용한 영상데이터의 효율적인 특징추출에 대하여 제안한다. 제안된 학습알고리즘에서는 최적해로 수렴하는 과정에서 발생할 수도 있는 진동을 억제하여 빠른 속도의 수렴이 가능하도록 하기 위해 모멘트를 이용하였고, 국소최적해를 만났을 때 이를 벗어난 전역최적해로의 수렴을 위한 새로운 연결가중치의 설정을 위하여 동적터널링을 이용함으로써 빠른 수렴속도로 전역최적해에 수렴되도록 학습시킬 수 있다. 제안된 학습알고리즘을 이용한 신경망을 256$\times$256 픽셀의 간암영상과 128$\times$128 픽셀의 얼굴영상을 대상으로 실험한 결과, 기울기하강의 학습알고리즘을 이용한 기존 비선형 주요성분분석 신경망보다 우수한 수렴성능과 특징추출성능이 있음을 확인 할 수 있었다.

• Journal of Communications and Networks
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• v.8 no.4
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• pp.432-441
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• 2006

As the community antenna television (CATV) networks becomes ubiquitous, instead of constructing an entirely new broadband network infrastructure, it has emerged as one of the rapid and economic technologies to interconnecting heterogeneous network to provide broadband access to subscribers. How to support ubiquitous real-time multimedia applications, especially in a heavy traffic environment, becomes a critical issue in modern CATV networks. In this paper, we propose a time guaranteed and efficient upstream minislots allocation algorithm for supporting quality-of-service (QoS) traffic over data over cable service interface specification (DOCSIS) CATV networks to fulfill the needs of realtime interactive services, such as video telephony, video on demand (VOD), distance learning, and so on. The proposed adaptive fast expansion algorithm and the loading statistics with dynamic swapping algorithm have been shown to perform better than that of the multimedia cable network system (MCNS) DOCSIS.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.960-962
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• 1999

The main idea of the proposed neuro-fuzzy system is conditional clustering whose main objective is to develop clusters preserving homogeneity of the clustered patterns with regard to their similarity in the input space as well as their respective values assumed in the output space. In the proposed neuro-fuzzy system, the structure identification is used with conditional fuzzy clustering, the parameter identification carried out by the hybrid learning scheme using back-propagation and total least squares.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.454-467
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• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.536-543
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• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.222-234
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• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.11-12
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• 2016

SVM (Support Vector machine) is powerful machine-learning method, and obtains better performance than traditional methods in the applications of muti-dimension nonlinear pattern classification. For the case of SVM model training and low efficiency in large samples, this paper proposes a combination of statistical parameters of the GMM-UBM (Universal Background Model) model. It is very effective to solve the problem of the large sample for the SVM training. The experiment is carried on four special dynamic hand gestures using the EPIC sensors. And the results show that the improved dynamic hand gesture recognition system has a high recognition rate up to 96.75%.