• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.239-246
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• 2010

This paper proposes a new document clustering method using NMF and fuzzy relationship. The proposed method can improve the quality of document clustering because the clustered documents by using fuzzy relation values between semantic features and terms to distinguish well dissimilar documents in clusters, the selected cluster label terms by using semantic features with NMF, which is used in document clustering, can represent an inherent structure of document set better. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.41-53
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• 2000

In this paper, the Maekjin and the Yangdorak Diagnosis algorithm by using a neuro-fuzzy method is proposed and it is implemented on the DSP-based system. Maekjin is measured by 3-channels of the Maekjin board through Maekjin probe which is attached on Chon, Kwan and Chuk of patient's wrist. First, we experiment Chon, Kwan and Chuk, 3-parts simultaneously and second perform one part of Chon, Kwan and Chuk respectively, The experimental results show that the Maekjin signal is measured precisely with any Maekjin probe. In Yangdorak diagnosis, the pulse generated by electric stimulator stimulates a portion of body and the response signal is measured through electrodes which is attached on representative points of 12 kyungmaks. The experimental methods are (1) 1 channel-measure, (2) 2 channels-measure, (3) 6 channels-measure and (4) 24 channels-measure. A fuzzy diagnosis is performed and neural networks is learned using fuzzy values as inputs, and we show that neuro-fuzzy diagnosis method is performed well.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.649-661
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• 2001

While the conventional edge detection can be considered as the problem of determining the existence of edges at certain locations, the fuzzy edge modeling can be considered as the problem of determining the membership values of edges. Thus, if the location of an edge is unclear, or if the intensity function is different from the ideal edge model, the degree of edgeness at the location is represented as a fuzzy membership value. Using the concept of fuzzy edgeness, an automatic smoothing parameter evaluation and selection method for a conventional edge detector is proposed. This evaluation method uses the fuzzy edge modeling, and can analyze the effect of smoothing parameter to determine an optimal parameter for a given image. By using the selected parameter we can detect least ambiguous edges of a detection method for an image. The effectiveness of the parameter evaluation method is analyzed and demonstrated using a set of synthetic and real images.

• Spatial Information Research
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• v.7 no.2
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• pp.191-207
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• 1999

Data and information necessary for solving the spatial decision making problems are imperfect or inaccurate and most are described by natural language. In order to process these arts of information by the computer, the obscure linguistic value need to be described quantitatively to let and computer understand natural language used by humans. For this , the fuzzy set theory and the fuzzy logic are used representative methodology. So this paper describes the construction of the language model by the natural language that user easily can understand and the logical concepts and construction process for building the fuzzy inference mechanism. It makes possible to solve the space related decision making problems intellectually through structuring and inference used by the computer, in case of the evaluation concern or decision making problems are described inaccurate, based on the inaccurate or indistinct data and information.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.117-127
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• 1998

Power systems have uncertain dynamics due to a variety of effects such as lightning, severe storms and equipment failures. The variation of the effective reactance of a transmission line due to a fault is an example of uncertainty in power system dynamics. Hence, a robust controller to cope with these uncertainties is needed. Recently, fuzzy controllers are becoming quite popular for robust control due to its potential of dealing with uncertain systems. Thus in this paper we design an adaptive fuzzy controller based on an input-output linearization approach for the transient stabilization and voltage regulation of a power system under a sudden fault. Also this paper proposes a fuzzy system that estimates the upper bound of uncertain term in the system dynamics to guarantee the Lyapunov stability. Simulation results show that good performance is achieved by the proposed controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.359-365
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• 2008

CP(Counterpropagation)알고리즘은 서로 다른 두 개의 신경망이 하나로 결합 된 혼합형 모델로서, 다른 신경망 모델에 비해 비교적 단순하고 빠른 학습 속도를 보인다. 그러나 CP 알고리즘은 다양한 패턴이 입력되면 충분한 경쟁층의 수가 설정되지 않아 학습이 불안정하고, 출력층에서 연결강도를 조정할 때 일반적인 학습률 조정방법으로 불안정한 학습 결과를 보인다. 이러한 문제점을 해결하기 위해 다수의 경쟁층을 설정하여 경쟁층에서 패턴 분류의 정확성을 높이고, 입력 벡터와 승자 뉴런의 대표 벡터간의 차이와 승자 빈도수를 반영하여 학습률을 동적으로 조정하여 경쟁층에서의 학습이 안정적으로 진행되도록 하고, 출력층에서 연결강도를 조정할 때 모멘텀(momentum)학습법을 적용한 개선된 CP 알고리즘이 제안되었다. 본 논문에서는 개선된 CP 알고리즘에서 경쟁층의 수를 효율적으로 설정하기 위해 퍼지 제어 기법을 이용하여 경쟁층의 수를 결정하는 방법을 제안한다. 제안된 방법은 CP 알고리즘에 입력되는 패턴의 정보를 이용하여 퍼지 소속 함수를 설계하고 입력에 대한 소속도를 계산한 후, 퍼지 제어 규칙을 적용하고, Mamdani의 Min_Max 추론 방법으로 추론한다. 퍼지 추론을 통해 최종적으로 얻어진 값을 무게 중심법으로 비퍼지화 하여 최종적으로 개선된 CP 알고리즘의 경쟁층의 수를 결정하는데 적용한다. 제안된 방법의 학습 및 인식 성능을 평가하기 위해, 숫자, 영어 등과 같이 다양한 패턴을 실험에 적용한 결과, 제안된 방법이 경쟁층의 수를 결정하는데 효과적임을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.309-312
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• 2011

본 논문에서는 유무선 통신환경의 클라이언트-서버 모델에서 바이오정보를 이용해 비밀 키 또는 비밀 정보를 실시간 공유할 수 있는 신뢰구간 기반의 바이오-암호 기법을 설계하고 분석한다. 제안하는 바이오-암호 기법은 키 결합 방식의 바이오-암호 기법의 대표적인 기법의 하나인 퍼지볼트 기법을 기본 모델로 두고 있으나, 퍼지볼트 기법의 주요 이슈인 거짓 볼트 수의 증가에 따른 인식률 저하, 고차 다항식 복원 시간의 실용성 여부, 상관 공격에의 취약을 해결하기 위하여 T-검정 기반 신뢰구간을 적용한 키 결합 방식으로 설계하였다. 실험결과는 거짓 볼트 수의 증가와 상관없이 항상 비밀 키를 획득해 보안성과 정확성이 향상되고, 이때의 수행시간도 사용자가 인지하기 힘든 작은 값으로 실시간 사용의 가능성을 높이고, 유사 바이오정보로 비밀 키를 획득할 수 없어 상관 공격에도 강인함을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.797-804
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• 2017

A social network is a typical social science environment with both network and iteration characteristics. This research presents a reaction analysis model of how each node responds to social networks when given input such as promotions or incentives. In addition, the setting value of a specific node is changed while examining the response of each node. And we try to understand the reactions of the nodes involved. The reaction analysis model is constructed by applying various techniques such as unidirectional, fuzzy set, weighting, and cyclic feedback, so it can accommodate the complicated environment of practice. Finally, the implementation model is implemented using Vensim rather than NetLogo because it requires repetitive input, change of setting value in real time, and analysis of association between nodes.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.29-39
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• 2006

Sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. A number of routing protocols to transmit the data between the base station and sensor nodes have been proposed. Intanagonwiwat et al. proposed the directed diffusion in which the base station sends interest messages and waits for data from the nodes in the specific regions. Since the directed diffusion propagates every interest message to whole nodes in the network, it causes energy dissipation of nodes. In this paper, we propose a novel data propagation method, which limits the data transmission area according to a threshold value for reducing the energy consumption in the network. A fuzzy logic is exploited to determine the threshold value by considering the energy and density of all the deployed nodes. The simulation models are designed and implemented based on DEVS formalism which is theoretically well grounded means of expressing discrete event simulation models.