• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.205-210
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• 2004

This paper deals with rule extraction from data using rough set theory. We construct the rule base in a hierarchical granulation structure by applying core as a classification criteria at each level. When more than one core exist, the coverage is used for the selection of an appropriate one among them to increase the classification rate and accuracy. In Addition, a probabilistic approach is suggested so that the partially useful information included in inconsistent data can be contributed to knowledge reduction in order to decrease the effect of the uncertainty or vagueness of data. As a result, the proposed method yields more proper and efficient rule base in compatability and size. The simulation result shows that it gives a good performance in spite of very simple rules and short conditionals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.3-6
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• 2008

베어링은 각종 설비에서 활용하는 중요한 기계요소 중 하나이다. 설비고장의 상당수는 베어링의 결함이나 파손에 기인하고 있다. 따라서 베어링에 대한 온라인모니터링기술은 설비의 정지를 예방하고 손실을 줄이는 데 필수적이다. 본 논문은 진동신호를 이용하여 베어링의 상태를 예측하기 위한 온라인모니터링에 대해 연구한다. 프로파일로 주어지는 진동신호는 이산웨이블릿변환을 통해 분석되고, 분해수준별 웨이블릿계수로부터 얻은 통계적 특징 중 유의한 것을 선별하고자 분산분석 (ANOVA)을 이용한다. 선별된 특징벡터는 Support Vector Machine (SVM)의 입력이 되는 데, 본 논문에서는 다중클래스 분류문제를 다루기 위한 계층적 SVM 네트워크를 제안한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Journal of Integrative Natural Science
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• v.9 no.3
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• pp.199-205
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• 2016

In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.69-72
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• 2006

급격한 정보화 산업의 발달로 인하여 혁신적인 기술 진화와 함께 이에 기반한 새로운 환경적, 기술적 패러다임이 변화되고 있다. 공간 간 융합과 조화를 극대화 시키고 공간속에서의 충돌과 문제점을 최소화시키기 위한 유비쿼터스 공간의 출현이다. USN에서 많은 수의 작고 다양하고 이질적인 센서 데이터 들이 발생하고 있다. 센서 데이터베이스 시스템에서 수많은 데이터들을 융합하기 위하여 에이전트 기술을 이용하고, 방대하고 애매모호한 데이터를 퍼지이론을 적용하여 데이터를 분류하여 적절한 장소에서 사용자의 욕구에 알맞은 정보를 제공함으로써 효율성과 융통성을 지원하는 방법을 제안한다. 본 논문에서는 이러한 애매모호한 데이터를 적절하게 분류함으로써 시간과 비용을 절약하고 빠른 응답을 사용자에게 전달할 수 있으며 유효적절한 서비스를 사용자의 기호에 맞게 제공함으로써 공간과 사물에 주어진 센서 데이터를 효율적으로 관리 할 수 있는 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.179-182
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• 2006

현재 감정을 인식할 수 있는 방법으로는 음성, 뇌파, 심박, 표정 등 많은 방법들이 존재한다. 본 논문은 이러한 방법 중 음성 신호를 이용한 방법으로써 특징들은 크게 피치, 에너지, 포만트 3가지 특징 점을 고려하였으며 이렇게 다양한 특징들을 사용하는 이유는 아직 획기적인 특징점이 정립되지 않았기 때문이며 이러한 선택의 문제를 해결하기 위해 본 논문에서는 특징 선택 방법 중 Multi Feature Selection(MFS) 방법을 사용하였으며 학습 알고리즘은 Self Organizing Map 알고리즘을 이용하여 음성 신호의 감정 특징 패턴을 분류하는 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.260-263
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• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has been used clinically, but nowadays we are mainly studying Brain-Computer Interface(BCI) such as interfacing with a computer through the EEG controlling the machine through the EEG The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.159-165
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• 2002

This paper proposes a GA and Gradient Descent Method-based method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to fond a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.