• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.337-340
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• 2007

Recently, autonomous parking problems have attracted a great deal of attention and have been examined in many papers in the literature. In this paper we design a fuzzy logic based garage parking system which is a important part for designing a autonomous parking system. We first analysis the existed papers and design a single-input fuzzy logic control for the parking algorithm and illustrate the effectiveness of the new method via the simulation results.

• Journal of Biosystems Engineering
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• v.20 no.1
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.81-91
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• 1996

This paper proposes an optimal COA(Center Of Area) defuzzification method that improves the contr~lp erformance of a fuzzy logic controller. The defuzzification method incorporates both the membership values and the effective span of membership function6 in calculating a crisp value. An optimal effective span is determined automatically by the genetic algorithm thrqugh the training of some typical examples. Simulation of the proposed COA defuzzifier to the truck backer-upper control problem is presented and the control performance of the praposed COA defuzzifier outperforms that of the conventional COA defuzzifier by more than 20% in terms of ayerage tracing distance.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.327-331
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• 2009

In fuzzy logic, aggregating uncertainties is generally achieved by means of operators such as t-norms and t-conorms. However, existing aggregation operators have some disadvantages as follows : First, they are situation-independent. Thus, they may not be properly applied to dynamic aggregation process. Second, they do not give an intuitional sense to decision making process. To solve these problems, we propose a new $\varepsilon$-AMDA (Aggregation based on the fuzzy Multidimensional Decision Analysis) algorithm to reflect degrees of strength for option i (i = 1, 2, ..., n) in the decision making process. The $\varepsilon$-AMDA algorithm makes adaptive aggregation results between min (the most weakness for an option) and max (the most strength for an option) according to the values of the parameter representing degrees of strength for an option. In this respect, it may be applied to dynamic aggregation process. In addition, it provides a mechanism of the fuzzy multidimensional decision analysis for decision making, and gives an intuitional sense to decision making process. Thus, the proposed method aids the decision maker to get a suitable decision according to the degrees of strength for options (or alternatives).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10B
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• pp.854-861
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• 2003

In this paper, we proposed the fuzzy control algorithm for efficient buffer control about traffic that is connected in ATM networks. The proposed Fuzzy control algorithm has total traffic arrival ratio, buffer occupancy ratio and Fuzzy set to search for dynamic service rates in server. That is, is based on Fuzzy logic according to the arrival ratio of total traffic and buffer occupancy ratio that is happened and reasoning. Then, made reasoning result control service rates in server about traffic that is connected with defuzzification value. Performance analysis result: it was confirmed that with the proposed scheme, performance improves at cell loss rate, when compared with the existing PBS scheme.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.371-373
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• 1999

축구경기는 세계적으로 널리 알려진 스포츠이다. 특히, 한국과 일본은 2002년 월드컵을 개최하는 나라이다. 이와 같은 배경에서, 우리는 퍼지 논리를 이용하여 축구 선수들의 기본적인 동작(Motion)을 인식하는 알고리즘을 제안한다. 여기서 기본 동작이란, 걷기, 뛰기, 드리블하기, 서있기 등으로 정의한다. 만약 우리가 축구경기 장면 비디오를 통해 동작 인식을 필요로 한다면, 특징 패턴은 축구선수들의 양다리 각과 한 축구선수와 공과의 거리가 중요 요소가 될 것이다. 이 논문은 축구 선수 및 공의 추적(Tracking)을 통해 전처리된 데이터를 얻을 수 있다고 가정한다. 실험결과를 통해 축구선수 뿐만 아니라 스포츠 경기에서 선수들의 기본 동작 인식에 퍼지 논리를 적용할 수 있음을 알 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.35-42
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• 1998

이동로봇이 주위환경에 대해서 전혀 알지 못하고 목표점으로 주행할때, 로봇은 긴 장애물이나 오목한 장애물에 대해서 존재할 수 있는 국부최소점에 빠져 더 이상 진행을 못하는 발생할수 있다. 본 논문에서는, 이 문제를 해결하기 위하여 퍼지 이론을 사용하여 효율적인 장애물회피와 안정된 목표점 도달을 달성할수 있도록하는 알고리즘을 제시한다. 제시된 알고리즘은 상하 2층 구조로 되어 있으며, 하위층은 장애물 회피 알고리즘 및 목표점 접근 알고리즘으로, 그리고 상위층은 로봇이 이동하면서 변화하는 환경에 맞게 앞의 두 알고리즘에 적절한 가중치를 부여하는 가중치 부여 알고리즘으로 구성되어 있다. 본 논문에서 제시된 알고리즘의 타당성을 보이기 위하여 'ㄹ', 'ㅊ' 형태의 장애물 및 여러 가지 형태의 장애물이 복합적으로 존재하는 환경에서 모의실험을 행한결과, 만족할 만한 결과를 얻을 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.106-110
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• 1996

고밀도 집적회로(VLSI)의 설계 과정에 있어 테스트(test)는 매우 중요한 과정으로서, 회로내의 결함(fault)을 찾기 위해 일련의 입력값을 넣어 그 출력값으로 고장 여부를 판단한다. 회로의 테스트를 위하여 사용되는 일련의 입력값을 테스트패턴(test pattern)이라 하며 최고 2n개의 테스트패턴이 생성될 수 있다. 그러므로 얼마나 작은 테스트패턴을 사용하여 회로의 결함 여부를 판단하느냐가 주된 관점이 된다. 기존의 테스트 패턴 생성 알고리즘인 휴리스틱(heuristic)조건에서 가장 큰 문제점은 빈번히 발생하는 백트랙(backtrack)과 이로 인한 시간과 기억장소의 낭비이다. 본 논문에서는 이러한 문제점을 보완하기 위해 퍼지 기법을 이용한 새로운 알고리즘을 제안한다. 제안된 기법에서는 고장신호 전파과정에서 여러개의 전파경로가 존재할 때, 가장 효율적인 경로를 선택하는 단계에서 퍼지 관계곱(Fuzzy Relational Product)을 이용한다. 이 퍼지 기법은 백트랙 수를 줄이고 기억장소와 시간의 낭비를 줄여 테스트 패턴 생성의 효율을 증가시킨다.