• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.

• Journal of Industrial Technology
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• v.17
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• pp.323-330
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• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.480-483
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• 2003

We propose a new framework that aims at multi-purpose image recognition, a difficult task for the conventional rule-based systems. This framework is farmed based on the idea of computer-based learning algorithm. In this research, we introduce the new functions of an additional learning and a knowledge reconstruction on the Fuzzy Inference Neural Network (FINN) (1) to enable the system to accommodate new objects and enhance the accuracy as necessary. We examine the capability of the proposed framework using two examples. The first one is the capital letter recognition task from UCI machine learning repository to estimate the effectiveness of the framework itself, Even though the whole training data was not given in advance, the proposed framework operated with a small loss of accuracy by introducing functions of the additional learning and the knowledge reconstruction. The other is the scenery image recognition. We confirmed that the proposed framework could recognize images with high accuracy and accommodate new object recursively.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.101-108
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• 2000

In this study, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river, This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.3
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• pp.252-262
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• 2007

In this paper, we suggest fuzzy control with feedforward compensator of superheat to progress both energy saving and coefficient of performance(COP) in a variable speed refrigeration system. The capacity and superheat are controlled simultaneously and independently by an inverter and an electronic expansion valve respectively for saving energy and improving COP in the system. By adopting the fuzzy control. the controller design for the capacity and superheat is possible without depending on a dynamic model of the system. Moreover, the feedforward compensator of the superheat can eliminate influence of the interfering loop between capacity and superheat. Some experiments are conducted to design the appropriate fuzzy controller by an iteration manner. The results show that the proposed fuzzy controller with the compensator can establish good control performances for the complicated refrigeration system with inherent strong non-linearity.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.486-495
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• 2001

This paper describes an implementation of the interval based expert system for syndrome differential diagnosis of Oriental Traditional Medicine (OTM). An approximate reasoning model using fuzzy logic for syndrome differential diagnosis is proposed. Based on this model, we implemented the system for diagnosing Eight rule diagnosis, organ diagnosis and then final differential syndrome of OTM. After carrying out inference process, the system will provide patient\`s syndromes differentiation diagnosis in the intervals and will give the explanation, which helps the user to understand the obtained conclusions.

• Korean Journal of Cognitive Science
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• v.15 no.1
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• pp.25-33
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• 2004

This paper discusses two problems with LR parsing with regard to constructing parsing tables with feature-based grammars. First, we show that traditional LR parsing methods suffer from nontermination and nondeterminism problems when they are applied to feature-based grammars. We then present an LR method for feature-based grammars that avoids both nontermination and nondetermisim by making use of partial information of a feature structure. Second, we describe the problem of adapting LR parsing to feature-based grammars with schematic rules (i.e., rules that do not contain enough information to construct parsing tables). To remedy this problem, we propose a rule inference algorithm which instantiates underspecified rules into more specified ones containing enough information.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.277-285
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• 2007

본 연구는 시맨틱웹 응용의 중심 기술인 웹 온톨로지의 표현과 추론을 위해 서술 논리와 규칙언어를 기반으로하는 웹 온톨로지 모델링 방법을 제안한다. 현재 웹 온톨로지 표현 언어인 OWL DL은 서술 논리에 근거하여 표현되는 것이나, 기계나 온톨로지 공학자가 OWL로 기술된 온톨로지를 직관적으로 이해하고 공유할 수 있는 형식적이고 명시적인 온톨로지의 지식 표현은 부족한 실정이다. 따라서 본 연구는 시맨틱웹이 목적하는 웹 온톨로지 구축을 위한 웹 온톨로지 모델링 방법으로 웹 온톨로지 모델링 계층을 제안하고, 제안된 각 계층에 따라 서술 논리의 TBox와 ABox의 구조와 SWRL을 기반으로 지식을 표현하는 웹 온톨로지 모델링 방법을 제안한다. 제안된 웹 온톨로지 모델링 방법의 성능 검증을 위해 제안 방법에 따라 웹 온톨로지를 구축하였고, SPARQL과 TopBraid의 DL Inference를 이용하여 구축된 웹 온톨로지의 성능을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.559-561
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• 2003

최근 차세대 Web 기술로서 Semantic Web이 주목 받고 있다. Semantic Web에서는 Web상에 존재하는 문서에 Web Resource들에 대한 Ontology를 기반으로 Semantic Annotation을 하고 Ontology 추론 Agent를 통하여 의미 기반으로 Web을 검색할 수 있도록 해준다. 이와 같은 Semantic Web 기술의 핵심 요소는 Web Ontology이며 W3C에서는 이를 표현 할 수 있는 표준 언어로서 RDF기반의 OWL(Web Ontology Language) 명세를 제정하고 있다. 따라서 표준 Web Ontology 언어인 OWL을 위한 추론 시스템은 Semantic Web 검색 Agent의 구현을 위한 필수적인 기반 기술이라 할 수 있으나 아직 그 개발이 미비한 상태이다. OWL 추론 시스템을 구현하기 위해서는 OWL의 이론적인 기반을 제공하는 DL(Description Logic)을 추론할 수 있는 엔진을 사용하는 것이 한가지 방법이 될 수 있으나 OWL이 Rule과 같은 DL의 범주를 벗어나는 Vocabulary를 지원하는 언어로 확장되는 경우에 이를 처리하기가 어렵다. 또 다른 방법으로서 Logic Programming을 통하여 OWL 언어의 Semantic을 기술하고 정리 증명(Theorem Proving)을 통하여 Ontology를 추론하는 공리화(Axiomatisation) 기법이 있는데 이러한 방법의 장점은 기반이 되는 Logic의 범주 내에서 새로운 언어를 위한 Vocabulary의 확장이 용이하다는 점이다. 본 논문에서는 Axiomatisation 방법을 이용하여 OWL로 기술된 Ontology를 추론할 수 있는 시스템의 설계 및 구현에 대해 설명하기로 한다.

• Fashion & Textile Research Journal
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• v.11 no.4
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• pp.648-660
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• 2009

In this research, an expert system has been developed to select optimum well-matched fusible interlinings with a face fabric. First, a database of face fabrics and fusible interlinings has been constructed. And knowledge acquisition has been performed from the previous studies about the properties of fusible interlinings and fused composites as well as fusing prsocess quality control. Then, a rule-based knowledge-base has been constructed through knowledge classification. Finally, we have constructed an inference engine with the knowledge-base. The expert system enables us to easily select optimum fusible interlinings for a face fabric considering high quality fused composites and fashion trend.