• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.223-230
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• 2004

In this paper, we will introduce a control strategy based on the permanent magnet linear synchronous motor (PMLSM) container transfer system using soft-computing algorithm. Linear motor-based container transport system (LMCTS) is horizontal transfer system for the yard automation, which has been proposed to take the place of automated guided vehicle in the maritime container terminal. LMCTS is considered as that the system is changed its model suddenly and variously by loading and unloading container. The proposed control system is consisted of two DR-FNNs that act the role of controller and system emulator. Consequently, the system had the predictable structure and an ability to adapt for a huge variation of rolling friction, detent force, and sudden changes of its weight by loading and unloading.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.233-238
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• 2003

Living creatures maintain their variety through speciation, which helps them to have more fitness for an environment. So evolutionary algorithm based on biological evolution must maintain variety in order to adapt to its environment. In this paper, we utilize the concept of speciation. Each individual of population creates their offsprings using mutation, and next generation consists of them. Each individual explores search space determined by mutation. Useful search space is extended by differentiation, then population explorers whole search space very effectively. If evolvable hardware evolves through mutation, it is useful way to explorer search space because of less varying inner structure. We verify the effectiveness of the proposed method by applying it to two optimization problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.95-100
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• 2010

This paper proposes a new spatial regularization of Fisher linear discriminant analysis (LDA) to reduce the overfitting due to small size sample (SSS) problem in face recognition. Many regularized LDAs have been proposed to alleviate the overfitting by regularizing an estimate of the within-class scatter matrix. Spatial regularization methods have been suggested that make the discriminant vectors spatially smooth, leading to mitigation of the overfitting. As a generalized version of the spatially regularized LDA, the proposed regularized LDA utilizes the non-uniformity of spatial correlation structures in face images in adding a spatial smoothness constraint into an LDA framework. The region-dependent spatial regularization is advantageous for capturing the non-flat spatial correlation structure within face image as well as obtaining a spatially smooth projection of LDA. Experimental results on public face databases such as ORL and CMU PIE show that the proposed regularized LDA performs well especially when the number of training images per individual is quite small, compared with other regularized LDAs.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.121-126
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• 1998

인간이 쾌적하다고 느끼게 되는 것에는 인간과 환경간의 상호작용이 포함되어 있다. 쾌적한 환경을 만든다고 할 때에는 인간과 환경을 하나의 시스템으로 두고 관련되는 여러 요소들이 계속적으로 피이드백 되는 것으로 생각하여야 한다. 쾌적감이란 여러 가지 감성 요소가 복합적으로 조합되어 하나의 이미지와 합치되는 것으로 표현되는 고도의 심리적인 체험감이다. 그러므로 쾌적환경에 대하여 인간의 특성을 중심으로 설계하려고 하면 인간이 쾌적환경을 인지하는 과정이 어떤 과정을 거치게 되는가를 알고 그 특성을 고려하여야 한다. 본 연구는 쾌적감을 구성하고 있는 요소이미지가 어떻게 구성되어 있으며, 환경요소에 대한 감각이미지와는 어떤 구조로서 이루어져 있는 지를 실험적으로 알아보고 퍼지 추론을 이용하여 표현하였다.

• Korean System Dynamics Review
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• v.1 no.1
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• pp.103-131
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• 2000

If a system such as a port has a large boundary and complexity, and the system's substance is considered as a black box, forecast accuracy will be very low. Futhermore various components in a port exert significant influence on each other. To copy with these problem the form of structure models were introduced by using SD method. The Competitive Ports Model had several sub-systems consisting of each Unit Port models, and each Unit Port model was made by quantitative, qualitative factors and their feedback loops. The fact that all components of one port have influence on the components of the other ports should be taken into account to construct Competitive Port Models. However, with the current approach that is impossible, and in this paper therefore, models were simplified by HFP adapted to integrate level variables of unit port models. Although many studies on modelling of port competitive situation have been conducted, both theoretical frame and methodology are still very weak. In this study, a new algorithm called ESD(Extensional System Dynamics) for the evaluation of port competition was presented, and applied to simulate port systems in northeast asia.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.313-321
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• 2014

In the current era of data-intensive services, the handling of big data is a crucial issue that affects almost every discipline and industry. In this study, we propose a classification method for large volumes of numeric data, which is implemented in a distributed programming framework, i.e., MapReduce. The proposed method partitions the data space into a grid structure and it then models the probability distributions of classes for grid cells by collecting sufficient statistics using distributed MapReduce tasks. The class labeling of new data is achieved by k-nearest neighbor classification based on Bayesian inference.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.1-10
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• 1996

There are many challenging problems in mobile robot navigation. As an example, a mobile robot may wander around in local minimum and may wiggle when it moves through a narrow corridor. In addition, the real time obstacle avoidance and the posture control of mobile robot are also very improtant problems. To address these problems, a navigation algorithm which is composed o freal time obstacle avoidance algorithm and a global path planner (GPP) that genrates the shortest path is presented. In this paper, the global path planner reduce the calculation time by reducing the dta to be handled. Also it can make a real time obstacle avoidance by using the fuzzy logic inference. So the presented algorithm provide a stable navigastion for the mobile robot when it fall into the unstable navigation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.99-102
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• 1998

본 논문에서는 복잡하고 비선형적인 시스템의 최적 모델링을 우해서 지능형 퍼지-뉴럴네트워크의 최적 모델 구축을 위한 방법을 제안한다. 기본 모델은 퍼지 추론 시스템의 언어적인 규칙생성의 장점과 뉴럴 네트워크의 학습기능을 결합한 FNNs 모델을 사용한다. FNNs 모델의 퍼지 추론부는 간략추론이 사용되고, 학습은 요류 역전파 알고리즘을 사용하여 다른 모델들에 비해 학습속도가 빠르고 수렴능력이 우수하다. 그러나 기본 모델은 주어진 시스템에 대하여 퍼지 공간을 균등하게 분할하여 퍼지 소속을 정의한다. 이것은 비선형 시스템의 모델링에 있어어서 성능을 저하시켜 최적의 모델을 얻기가 어렵다. 논문에서는 주어진 데이터의 특성을 부여한 공간을 설정하기 위하여 클러스터링 알고리즘을 사용한다. 클러스터링 알고리즘은 주어진 시스템에 대하여 상호 연관성이 있는 데이터들끼리 특성을 나누어 몇 개의 클래스를 이룬다. 클러스터링 알고리즘을 사용하여 초기 FNNs 모델의 퍼지 공간을 나누고 소속함수를 정의한다. 또한, 최적화 기법중의 하나로 자연선택과 자연계의 유전자 메카니즘에 바탕을 둔 탐색 알고리즘인 유전자 알고리즘을 사용하여 주\ulcorner 진 모델에 대하여 최적화를 수행한다. 또한 본 연구에서는 학습 및 테스트 데이터의 성능 결과의 상호 균형을 얻기 위한 하중값을 가긴 성능지수가 제시된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.