• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• 한국정보통신학회논문지
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• 제7권7호
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• pp.1520-1526
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• 2003

본 논문에서는 마이크로프로세서를 이용하여 소비전력 모니터링이 가능한 100[W]급 직류입력/교류출력변환 인버터를 설계하였다. 또한 입출력 제어와 소비전력정보 모니터링을 외부환경에 적응적으로 대응하기 위해 퍼지추론 시스템을 설계하였다. 추론결과를 PIC16C711 프로세서에서 처리함으로서 입력전압의 변화와 소자의 온도특성 등에 적응적인 인버터 설계가 가능함을 보였다. 제작된 시스템을 이용하여 효율실험 및 부하실험을 수행하였고 오차는 2% 이내임을 확인하였다.

• 한국항행학회논문지
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• 제13권6호
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• pp.901-906
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• 2009

본 논문에서는 퍼지추론과 신경망 학습을 이용하여 무선망의 핸드오프호 손실율 가능성 분포의 상한계 값을 추정하고, 이를 기반으로 한 호 수락제어를 제안한다. 퍼지규칙의 소속함수는 신경망 오류역전파 알고리즘으로 튜닝하고, 핸드오프호 손실율의 상한계 값은 퍼지집합의 가중치 평균에 기초하여 추정한다. 이 방법은 호 손실율 값을 실제보다 과도하게 큰 값으로 추정하는 것을 방지하고, 추정된 값이 실제값보다 작을 때는 실시간적으로 자기보상을 실시하여 호 수락 제어시 호 손실율을 줄인다. 시뮬레이션을 통하여 제안한 방법에 의한 핸드오프 호 손실율의 상한계 값의 추정성능을 보이고, 이를 이용한 호 수락제어 방법이 핸드오프 호 손실율을 사용자가 원하는 핸드오프 호 손실율 이하로 유지할 수 있음을 보인다.

• 한국정보통신학회논문지
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• 제17권7호
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• pp.1732-1739
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• 2013

인간의 한쪽 염색체상에 나타나는 SNP의 서열인 일배체형을 식별해내면 효과적인 유전질병 연관검사를 할 수 있다. 일배체형 추론문제란 특정 집단의 유전자형 집합으로부터 집단에 속한 각 개체의 유전자형을 설명할 수 있는 일배체형 집합을 도출해내는 것을 말한다. 본 논문에서는 검약기반 일배체형 추론 문제에 대해 최종 결과에 기여하지 않는 일배체형 집합을 후보군에서 제외함으로써 일배체형 추론과정에서 탐색해야 할 후보 일배체형의 개수를 줄이는 사전처리 알고리즘을 제시한다. 제시된 알고리즘은 기존의 사전처리 알고리즘에 비해 매우 빠르게 수행되며, 제시된 사전처리 알고리즘의 결과를 적용한 일배체형 추론은 대다수의 경우에 최적해를 산출하고, 최적해를 산출하지 않는 경우에도 최적해의 일배체형 개수와 크게 차이나지 않음을 실험을 통해서 보인다.

• 한국산업융합학회 논문집
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• 제21권3호
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.841-847
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• 1999

For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential. Such informations can be acquired from visual sensors. For robotic assembly, the corrective assembly motion to compensate for such misalignments has to be determined from the measured informations. However, this may not be simply derived from the measured misalignment alone because the part deformation progressively occurs during misalignment compensation. Based on the analysis of flexible parts assembly process, this paper presents a neural net-based inference system that can infer the complex relationship between the corrective motion and the measured information of parts deformation and misalignments. And it verifies the performance of the implemented inference system. The results show that the proposed neural net-based misalignment compensation algorithm Is effective in compensating for the lateral misalignment, and that it can be extended to the assembly tasks under more general conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.252-254
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• 2001

Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults(HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1658-1661
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• 2002

In this paper, we propose the intelligent inference trace algorithm of the mobile robot using fuzzy logic. With the proposed algorithm, the mobile robot can trace human at regular intervals. The mobile robot can recognize the distances between it and human with both multi-ultrasonic sensors and PC-camera and then, can inference the direction and velocity of itself to keep the given regular distances. In the first, the mobile robot acquires the information about circumstances using ultrasonic sensor and PC-camera then secondly, recognize the status of circumstances using the fuzzy logic. We also evaluate the experimental navigation test at several times to verify the ability of the fuzzy logic controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.999-1001
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• 1995

In this study, the algorithm of optimal parameter inference is proposed. At this inferring method, we tried to acquire the follow-up to reference pattern through comparing the plant output pattern with random reference pattern. As an inference method, the fuzzy theory was applied and the proposed algorithm was proved by computer simulation.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.