• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.138-145
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• 2001

In this paper, we propose fuzzy closed BCMP queueing network model for the performance evaluation of distributed processing system. Which has the ambiguous service requirements of job to servers and service rates of server according to network environments. This model can derive the measures for system Performances using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed for verifying the effectiveness of derived equations of performance evaluation according to service requirements of job and the numbers of clients.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.12-18
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• 2011

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.147-150
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• 2009

최근 새롭게 등장한 무선 센서 네트워크는 기존의 네트워크와는 다르게 통신 인프라가 없는 환경에서도 동작이 가능한 저전력 소출력의 무선 센서간의 네트워크를 형성하고 이들간의 정보 유통이 이루어진다. 무선 센서 네트워크는 열린 환경에서 배치되기 때문에 물리적 공격에 취약하다. 공격자는 손쉽게 노드들을 포획할 수 있으며 포획된 노드를 통해 허위 보고서를 네트워크에 주입할 수 있다. 허위 보고서 삽입 공격은 허위 경보를 유발할 뿐만 아니라 네트워크의 제한된 에너지를 고갈시킨다. 이러한 허위 보고서를 조기에 탐지 및 폐기하기 위하여 Yu와 Guan은 동적 여과 프로토골(dynamic on-route filtering scheme)를 제안하였다. 그러나 무선 센서 노드는 오직 제한된 진력자원으로 이루어져 있기 때문에 전력보존과 전력관리가 중요시 여겨진다. 본 논문에서는 동적 여과 프로토콜에서 허위 보고서 주입 공격에 대한 충분한 보안 강도 제공과 에너지 효율성을 위한 기법을 제안한다.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.595-604
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• 2004

By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.45-52
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• 2002

This paper proposes the fuzzy closed RCMP queueing network model using fuzzy set theory for the performance evaluation of centralized distributed processing system with ambiguous system factors in the network environments. This model can derive the measures for system performances such as the job spending time, the system throughput, average job number and server utilizations using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed centralized distributed system with fuzzy service requirement time for verifying the effectiveness of derived equations of performance evaluation according to the numbers of clients, and the results were analyzed. The proposed model provides more and flexible realistic than performance evaluation of conventional method when we evaluated system performance with ambiguous factors.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2089-2090
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• 2006

본 논문은 기호 코딩 및 정보입자를 이용한 유전자 알고리즘의 전방향 퍼지 집합 기반 뉴럴네트워크 (Information Granules and Symbolic Encoding-based Fuzzy Set Polynomial Neural Networks ; IG and SE based FSPNN)의 모델 설계를 제안한다. 기존 퍼지 집합기반 다항식 뉴럴네트워크(FSPNN)의 구조 최적화를 위해 이진코딩을 사용하였다. 그러나 이진코딩에서 스트링의 길이가 길면 길수록 인접한 두 수 사이에 발생하는 급격한 비트 차이라는 해밍절벽이 발생하였다. 이에 제안된 모델에서는 해밍절벽의 문제를 해결하기 위해 기호코딩을 사용하였다. 제안된 모델은 각 입력에 대해 MFs의 개수 만큼 규칙을 생성하는 Fuzzy 집합기반 다항식 뉴럴네트워크(FSPNN)를 그대로 사용한다. 그리고 IG based gFSPNN의 평가을 위해 실험적 예제를 통하여 제안된 모델의 성능 및 근사화 능력의 우수함을 보인다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.387-395
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• 2006

홈 네트워크는 유비쿼터스의 여러 응용분야 중 활발하게 연구 구현되고 있는 분야 중의 하나로, 최근 아파트 건설업체, 가전기기 업체, 통신서비스 업체들이 지능성을 갖춘 스마트 홈을 목적으로 다양한 시험제품과 솔루션을 출시 흑은 제시하면서 이를 자사의 중요한 마케팅전략으로 활용하고 있다. 스마트 홈 네트워크를 구성하는 다양한 스마트 홈 기기의 제품 경쟁력은 제품이 얼마나 지능성을 갖추어 사용자에게 편리성과 유용성을 제공할 수 있는가의 여부가 중요한 관건이 되고 있다. 따라서 지능형 홈 기기의 지능에 대한 측정기준 마련이 필요한 시점이다. 본 연구에서는 스마트 홈 네트워크를 구성하는 여러 요소 중에서 정보가전기기를 지능성 측정 대상으로 하여 지능등급 부여모델을 개발하고자 한다. 지능형 홈 기기의 지능을 측정하기 위하여 로봇분야의 다양한 문헌 고찰을 토대로 지능성 측정에 필요한 핵심 구성요소를 도출 및 재 정의하여 등급모델을 설계하였다. 특히 설계된 등급부여모델의 실질적 이용을 위해서는 평가방식에 있어서 계량화 절차가 요구된다. 따라서 평가모델의 특성상 다차원적인 지능성의 속성을 총합적으로 나타내기 위하여 퍼지이론(Fuzzy Theory)을 사용하였으며, 이를 정규화하기 위해 퍼지적분(Fuzzy Integral)을 이용하였다. 산출된 적분값을 다시 비퍼지화하여 지능성 등급을 부여하는 모델을 개발하였다. 제시된 지능성 등급부여 모델은 스마트 홈 네트워크 산업의 발전을 촉진하는 계기가 될 수 있으리라 기대한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.4
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• pp.19-25
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• 2008

This paper proposes a hierarchically structured navigation algorithm for autonomous mobile robot under unknown environment based on fuzzy-neal network. The proposed algorithm consists of two basic layers as follows. The lower layer consists of two parts such as fuzzy algorithm for goal approach and fuzzy-neural algorithm for obstacle avoidance. The upper layer which is basically fuzzy algorithm adjusts the magnitude of the weighting factor depending on the environmental situation. The proposed algorithm provides an efficient method to escape local mimimum points as shown in the simulation result. Most simulation results show that this algorithm is very effective for autonomous mobile robots' traveling in unknown field.