• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.108-114
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• 2008

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.94-99
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• 1993

In this paper, the method for navigation and obstacle avoidance of an autonomous mobile robot is proposed. It is based on the fuzzy inference system which enables to deal with imprecise and uncertain information, and on the neural network which enables to learn input and output pattern data obtained from ultrasonic sensors. For autonomous navigation, the wall-following navigation utilizing input and output data by an expert's control action is constructed. An approach by the neural network is developed for the obstacle avoidance because of the redundant input data. For an autonomous navigation, the fuzzy control and the control of the neural network are integrated and its feasibility is demonstrated by means of experiment.

• Journal of Communications and Networks
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• 제12권6호
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• pp.624-631
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• 2010

Effective traffic matrix estimation is the basis of efficient traffic engineering, and therefore, quality of service provision support in IP networks. In this study, traffic matrix estimation is investigated in IP networks and an Elman neural network-based traffic matrix inference (ENNTMI) method is proposed. In ENNTMI, the conventional Elman neural network is modified to capture the spatio-temporal correlations and the time-varying property, and certain side information is introduced to help estimate traffic matrix in a network accurately. The regular parameter is further introduced into the optimal equation. Thus, the highly ill-posed nature of traffic matrix estimation is overcome effectively and efficiently.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 2010년도 춘계국제학술대회
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• pp.277-287
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• 2010

We are sometimes interacting with people who we know nothing and facing with the difficult task of making decisions involving risk in social network. To reduce risk, the topic of building Web of trust is receiving considerable attention in social network. The easiest approach to build Web of trust will be to ask users to represent level of trust explicitly toward another users. However, there exists sparsity issue in Web of trust which is represented explicitly by users as well as it is difficult to urge users to express their level of trustworthiness. We propose a fuzzy-based inference model for Web of trust using user behavior information in social network. According to the experiment result which is applied in Epinions.com, the proposed model show improved connectivity in resulting Web of trust as well as reduced prediction error of trustworthiness compared to existing computational model.

• 전기학회논문지
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• 제56권12호
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• pp.2084-2090
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• 2007

The DGA (Dissolved Gases Analysis) technique has been widely using for fault diagnosis of the power transformers. Some electric power utility company establishes the criteria of DGA to improve reliability, because of difference of operation environment and design of power transformer. In this paper, we introduce intelligent diagnosis system for DGA result of KEPCO (Korea Electric Power Cooperation). This system can classify patterns type of gases ratio that frequently occurs in recent result of gases analysis using Fuzzy Inference. The classification of Patterns let us know that major causes of gases generation based on type of patterns. Finally, Neural Network based on patterns diagnose transformer. NN was trained using result data of DGA of actually faulted transformers recently. Result of intelligent diagnosis system is right well in comparison with actual inner inspection of transformers.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1889-1890
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• 2008

본 연구에서는 퍼지 추론 메커니즘에 기반 한 Polynomial RBF Neural Network(p-RBFNN)를 설계하고 얼굴인식 문제로 적용하여 분류기로서의 성능을 분석한다. 제안된 p-RBFNN 구조는 FCM 클러스터링에 기반 한 분할 함수를 활성 함수로 사용하며, 다항식 함수로 구성된 연결가중치를 사용함으로서 기존 신경회로망 분류기의 선형적인 특성을 개선한다. p-RBFNN 구조는 언어적 해석관점에서 "If-then"의 퍼지 규칙으로 표현되며 퍼지 추론 메커니즘에 의해 구동된다. 즉 조건부, 결론부, 추론부 세 가지의 기능적 모듈로 나뉘어 네트워크 구조가 형성된다. 조건부는 FCM 클러스터링을 사용하여 입력 공간을 분할하고, 결론부는 분할된 로컬 영역을 다항식 함수로 표현한다. 마지막으로, 네트워크의 최종출력은 추론부의 퍼지추론에 의한다. 또한 제안된 p-RBFNN을 얼굴인식 문제로 적용하여 성능을 분석한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전력기술부문
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• pp.319-322
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• 2002

Distance relay is operated in calculating line impedance. It can be worked accurately in overhead line. However, power cables or combined transmission lines need compensation for calculated impedance because cable systems have sheaths, grounding wires and sheath voltage limiters(SVLs) Neuro-fuzzy can be viewed either as a fuzay system, a neural network or fuzzy neural network and it can estimate the location of the fault accurately. In this paper, fault section and fault location can be classified and estimated in neuro- fuzzy inference system and neural network.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.664-669
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• 1994

This paper focuses on development and implementation of a performance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisgy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network perfomance manager that adjusts queue apacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.394-396
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• 1997

This paper describes RBF network controller based on fuzzy control rules for intelligent control of nonlinear systems. The proposed scheme is derived from the functional equivalence between RBF networks and fuzzy inference systems. The design procedure of the proposed scheme is realized by first transforming the fuzzy control rules into the parameters of RBF networks. The optimized RBF network controller is then performed through the gradient descent learning mechanism to an error function. The proposed method is rigorously tested using a nonlinear and unstable nonlinear system. Simulation is performed to demonstrate the feasibility and effectiveness of the proposed scheme.