• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.1
• /
• pp.10-14
• /
• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.10 no.1
• /
• pp.31-36
• /
• 2010

Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.4
• /
• pp.254-259
• /
• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.61 no.4
• /
• pp.228-233
• /
• 2012

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for monitoring of power transformer by FCM(Fuzzy c-means) and ELM(Extreme Learning Machine). The proposed technique make it possible to diagnosis the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.12
• /
• pp.2281-2284
• /
• 2008

The purpose of this paper is to develope the drowsiness-drive perception system which judges drowsiness driving based on drivers' eye region using single vision system. To do this, first, we use the Haar-like feature and AdaBoost learning algorithm for detecting the features of the face region. And we measure the eye blinking frequency and eye closure duration from these feature data. And then, we propose the drowsiness-drive detection algorithm using the eye blinking frequency and eye closure duration. Finally, we have shown the effectiveness and feasibility of the proposed method through some experiments.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.17-19
• /
• 2005

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and artificial neural network (ANN) control. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid Intelligent control

• Journal of Electrical Engineering and information Science
• /
• v.2 no.6
• /
• pp.177-182
• /
• 1997

A visual servoing method is proposed for a robot with a camera in hand. Specifically, vanishing point features are suggested by employing a viewing model of perspective projection to calculate the relative rolling, pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2995-2997
• /
• 2000

원자력발전소의 열출력 계산 결과에 가장 큰 영향을 미치는 변수는 주급수 유량이며, 측정방식상의 특성(Venturi Fouling)으로 인해 계산시 과다하게 반영될 소지가 있다 본 연구에서는 이 측정 오차를 최소화하기 위하여 뉴로-퍼지 논리를 이용하여 주급수 유량을 예측한 후 그 결과를 통해 열출력을 재평가하고자 하였다. 즉, 뉴로-퍼지로의 입력 변수(증기발생기 압력 및 수위. 터빈 충동실 압력)들은 모의훈련으로 출력을 상승시키면서 취득한 후 Wavelet Denoising 기법을 이용하여 노이즈를 제거시키고. 뉴로-퍼지 추론 계통의 파라메타들을 최적화시키기 위하여 유전적 알고리듬 및 최소자승법에 의한 Hybrid Learning Rule을 이용하여 학습시켰다. 시뮬레이션을 수행한 결과, 주급수 유량이 양호하게 예측되어, 이 결과를 토대로 열출력을 평가하는데 본 알고리듬의 적용이 성공적임을 입증하였다.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2374-2376
• /
• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.714-716
• /
• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.