• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.1093-1102
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• 2003

The dynamic nature of the World Wide Web challenges Search engines to find relevant and recent pages. Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. In this paper we study way spiders that should visit the URLs in order to obtain more “important” pages first. We define and apply several metrics, ranking formula for improving crawling results. The comparison between our result and Breadth-first Search (BFS) method shows the efficiency of our experiment system.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.844-855
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• 2005

This paper proposes a human action recognition scheme for Intelligent Web Browser. Based on the principle that a human action can be defined as a combination of multiple articulation movements, the inference of stochastic grammars is applied to recognize each action. Human actions in 3 dimensional (3D) world coordinate are measured, quantized and made into two sets of 4-chain-code for xy and zy projection planes, consequently they are appropriate for applying the stochastic grammar inference method. We confirm this method by experiments, that various physical actions can be classified correctly against a set of real world 3D temporal data. The result revealed a comparatively successful achievement of $93.8\%$ recognition rate through the experiments of 8 movements of human head and $84.9\%$ recognition rate of 60 movements of human upper body. We expect that this scheme can be used for human-machine interaction commands in a web browser.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1111-1116
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• 1993

In this study, we discuss the design of the expert system for the scheduling of the FMC(Flexible Manufacturing Cell) consisting of the several versatile machines. Due to the NP property, the scheduling problem of several machine FMC is very complex task. Thus we proposed the two heuritstic shceduling algorithms for solving the problem and constituted the algorithm based of solving the problem and constituted the algorithm base of ISS(Intelligent Scheduling System) using them. By the rules in the rule base, the best alternative among various algorithms in algorithm base is selected and applied in controlling the FMC. To show the efficiency of ISS, the scheduling output of ISS and the existent dynamic dispatching rule were tested and compared. The results indicate that the ISS is superior to the existent dynamic dispatching rules in various performance indexes.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.302-307
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• 2005

A novel neuro controller based simple neuro-structure with modified error function is introduced in this paper. This controller consists of two independent controllers, known as the voltage regulator and the angular controller. The voltage regulator is used to modify terminal voltage for the purpose of tracking a reference voltage. The angular controller is utilized to guarantee the stability of the system. In this structure each neuron uses a linear hard limit activation function that depends on the controlled variable and its derivatives. There is no need for parameter identification or any off-line training data. Two proposed controllers are merged by a smooth switch to build a complete controller. The effectiveness of the proposed novel control action is demonstrated through some computer simulations on a Single-Machine Infinite-Bus (SMIB) power system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.369-374
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• 2004

A cluster merging algorithm that merges convex clusters resulted by the Fuzzy Convex Clustering(FCC) method into non-convex clusters was proposed. This was achieved by proposing a fast and reliable distance measure between two convex clusters using Support Vector Machines(SVM) to improve accuracy and speed over other existing conventional methods. In doing so, it was possible to reduce cluster number without losing its representation of the data. In this paper, results for several data sets are given to show the validity of our distance measure and algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.175-178
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• 1997

최근들어 가전제품은 90년대를 전후로 고품질화, 고기능화, 다양화, 지능화로의 추세가 한층 가속화되도 있다. 즉 퍼지, 신경회로망, 카오스, 유전자 알고리즘등으로 대표되는 soft computing 기술을 적용하여 가전제품의 인공지능화를 추구해 왔으며 한편으로는 첨단이론을 적요안 가전제품의 수명은 점점 단축되고 있는 실정이다. 한편 환경보호에 대한 사회 전반적인 인식의 확대호 에너지 절약에 대한 관심이 고조되고 있다. 따라서 세탁기 사용에 있어서 세탁량을 정확히 감지하여 오감지로 인한 과도한 세탁수 사용을 방지할 수 있는 알고리즘을 개발하면 한정된 에너지를 절약하는데 큰 기여를 할 수 있다. Soft computing 기술의 하나인 Rough set 이론을 적용하여 세탁량(포량)감지 알고리즘개발에 관해 기술한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.381-384
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• 2006

Self Organizing Map(SOM) is a powerful neural network model for unsupervised loaming. In many clustering works with exploratory data analysis, it has been popularly used. But it has a weakness which is the poorly theoretical base. A lot more researches for settling the problem have been published. Also, our paper proposes a method to overcome the drawback of SOM. As compared with the presented researches, our method has a different approach to solve the problem. So, a hybrid SOM is proposed in this paper. Using Monte Carlo computing, a hybrid SOM improves the performance of clustering. We verify the improved performance of a hybrid SOM according to the experimental results using UCI machine loaming repository. In addition to, the number of clusters is determined by our hybrid SOM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.499-501
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• 2004

웹 로그, 바이오정보학 둥 여러 분야에서 다양한 형태의 결측치가 발생하여 학습 데이터를 희소하게 만든다. 결측치는 주로 전처리 과정에서 조건부 평균이나 나무 모형과 같은 기본적인 Imputation 방법을 이용하여 추정된 값에 의해 대체되기도 하고 일부는 제거되기도 한다. 특히, 결측치 비율이 매우 크게 되면 기존의 결측치 대체 방법의 정확도는 떨어진다. 또한 데이터의 결측치 비율이 증가할수록 사용 가능한 Imputation 방법들의 수는 극히 제한된다. 이러한 문제점을 해결하기 위하여 본 논문에서는 Vapnik의 Support Vector Regression을 데이터 전처리 과정에 알맞게 변형한 Support Vector Regression을 제안하여 이러한 문제점들을 해결하였다. 제안 방법을 통하여 결측치의 비율이 상당히 큰 희소 데이터의 전처리도 가능하게 되었다. UCI machine learning repository로부터 얻어진 데이터를 이용하여 제안 방법의 성능을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.642-647
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• 2004

The relations of fuzzy entropy, distance measure, and similarity measure are discussed in this paper. For the purpose of reliable signal selection, the fuzzy entropy is proposed by a distance measure. Properness of the proposed entropy is verified by the definition of the entropy measure. Fourier and Wavelet transform are applied to the stator current signal to obtain the fault features of an induction motor. Membership functions for 3-phase currents are obtained by the Bootstrap method and Central Limit Theorem. Finally, the proposed entropy is applied to measure the fault signal of an induction machine, and the fuzzy entropy values of phase currents are illustrated.