• 디지털콘텐츠학회 논문지
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• 제5권2호
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• pp.128-132
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• 2004

In this paper, a Fuzzy Inference System(FIS) based watermarking algorithm in Discrete Wavelet Transform(DWT) domain is proposed. A 2D fuzzy inference values, in which the inputs are parameters of the coefficients of the DWT block of the original image and the output is strength of watermark embedded, is devised. The fuzzy inference algorithm guarantees that the watermark to be embedded into the original image adaptively. The experimental results shows that proposed approach is robust to the digital image processing schemes.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권9호
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• pp.423-429
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• 2001

This paper proposes adaptive impedance control algorithm using fuzzy inference when robot contacts with its environments. The characteristics of the adaptive impedance controller is to adapt with parametric uncertainty and nonlinear conditions. The control algorithm is to join impedance controller with fuzzy inference engine. The proposed control method overcomes the problem of impedance controller using gain-tuning algorithm of fuzzy inference engine. We implemented an experimental set-up consisting of environment-generated one-link robot system and DSP system for controller development. We apply the adaptive fuzzy impedance controller to one-link root system, and it shows the good performance on regulating the interactive force in case of contacting with arbitrary environment.

• 한국지능시스템학회논문지
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• 제2권3호
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• pp.58-67
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• 1992

This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.173-176
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• 1999

This paper shows a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a Polishing robot. Using this method, the number of inference rules and the shape of membership functions are determined by the genetic algorithm. The fuzzy outputs of the consequent part are derived by the gradient descent method. Also, it is guaranteed that .the selected solution become the global optimal solution by optimizing the Akaike's information criterion expressing the quality of the inference rules. It is shown by simulations that the method of fuzzy inference by the genetic algorithm provides better learning capability than the trial and error method.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• 지능정보연구
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• 제13권3호
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• pp.17-29
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• 2007

웹 표준 언어인 XML에 기반한 각종 표준들을 바탕으로 소프트웨어 에이전트와의 인터페이스에 초점을 맞추고 있는 차세대 웹에서 소프트웨어 에이전트의 두뇌 역할을 수행하기 위한 추론엔진은 시맨틱 웹(Semantic Web)에서의 규칙 표현을 위한 언어인 SWRL(Semantic Web Rule Language)을 이해할 수 있어야 한다. 본 연구에서는 SWRL을 규칙 표현 방법으로 사용하고, OWL을 사실 표현 방법으로 사용하는 정방향 추론엔진인 SMART-F(SeMantic web Agent Reasoning Tools-Forward chaining inference engine)을 개발하고자 한다. 전통적인 규칙 추론 분야에서는 정방향 추론을 위하여 if-then 형태의 규칙을 네트워크 구조로 변환하여 정방향 규칙 추론의 효율성을 높인 Rete 알고리즘이 많이 사용되고 있다. 이를 시맨틱 웹 환경에 적용하기 위하여 SWRL 기반 정방향 추론을 위한 요구 기능을 분석하고, Rete 알고리즘에 도출된 차세대 시맨틱 웹의 요구 기능을 반영한 정방향 추론 알고리즘을 설계하였다. 또한, 유비쿼터스 환경에서의 각종 플랫폼의 독립성과 이식성을 확보하고 기기간의 성능 차이를 극복할 수 있도록 사실 베이스 및 규칙 베이스의 관리도구와 정방향 추론 엔진 등을 Java 컴포넌트로 개발하였으며, 이는 이미 개발된 역방향 추론엔진인 SMART-B와 규칙 베이스 및 사실 베이스를 완벽하게 호환 가능하므로 차세대 웹 환경에서의 지식 활용을 극대화시킬 것이다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
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• pp.263-266
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• 2007

본 논문은 베이지안 통계 추론에 대하여 논의한다. 논문은 베이지안 추론, Markov Chain과 Monte Carlo 적분, MCMC(Markov Chain Monte Carlo) 기법, Metropolis-Hastings 알고리즘, Gibbs 샘플링, Maximum Likelihood Estimation, EM 알고리즘, 상실된 데이터 보완 기법, BMA(Bayesian Model Averaging) 순서로 논의를 진행한다. 이러한 통계적 기법들은 대용량의 데이터를 처리하는 생물학, 의학, 생명 공학, 과학과 공학, 그리고 일반 데이터 조사와 처리 등에 사용되고 있으며, 최적의 추론 결과를 이끌어 내는데 중요한 방법을 제공하고 있다. 그리고 마지막으로 PC(Principal Component) 분석 기법에 대하여 논의한다. PC 분석 기법도 데이터 분석과 연구에 많이 활용된다.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2004년도 e-Biz World Conference
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• pp.134-149
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• 2004

This paper is aimed at proposing a fuzzy inference mechanism to enhancing the quality of cognitive map-based inference. Its main virtue lies in the two mechanisms： (1) a mechanism for avoiding a synchronization problem which is often observed during inference process with traditional cognitive map, and (2) a mechanism for fuzzifying decision maker's subjective judgment. Our proposed fuzzy inference mechanism (FIM) is basically based on the cognitive map stratification algorithm which can stratify a cognitive map into number of strata and then overcome the synchronization problem successfully. Besides, the proposed FIM depends on fuzzy membership function which is administered by decision maker. With an illustrative B2B negotiation problem, we applied the proposed FIM, deducing theoretical and practical implications. Implementation was conducted by Matlab language.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.