• Journal of Information Processing Systems
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• v.11 no.2
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• pp.184-204
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• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.451-456
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• 2007

유비쿼터스 컴퓨팅 환경을 구축하기 위해서는 사용자 및 주변 상황에 관한 인지기술이 필수적이다. 이에 따라 이기종 분산형 시스템에서 언어와 기종에 영향을 받지 않고 사용자 Context를 인지하고 표현하는 문제는 해결해야할 중요한 과제로 대두되었다. 이에 따라, 본 논문에서는 이 과제를 해결하기 위하여 시맨틱 웹 기술 및 퍼지 개념을 이용하여 사용자 Context를 기술하는 것을 제안한다. 온톨로지는 컴퓨터가 정보자원의 의미를 파악하고 자동적으로 처리할 수 있도록 고안된 지식표현 언어이므로 이기종 시스템 하에서의 사용자 Context를 표현하는데 적합하다. 한편, 사용자가 접할 실세계의 환경은 일반집합(Crisp Set)으로 표현하기 힘들기 때문에 본 논문에서는 퍼지개념과 표준 웹 온톨로지 언어 OWL이 융합된 Fuzzy OWL언어를 사용했다. 본 논문에서 제안하는 방법은 Context를 Fuzzy OWL로 표현하기 위하여 먼저 사용자가 접한 환경정보들을 수치로 표현한다. 그리고 이를 OWL로 기술하며 OWL로 표현된 사용자 Context를 Fuzzy OWL로 변환한다. 마지막으로 퍼지 개념이 포함된 사용자 Context를 이용하여 자동적인 상황인지가 가능한지 여부를 퍼지 추론 엔진인 FiRE를 사용하여 실험한다. 본 논문에서 제시한 방법을 사용하면 이기종 분산시스템에서도 사용할 수 있는 형태로 Context를 기술할 수 있다. 그리고 기술된 Context를 기반으로 현재 사용자가 접한 환경의 상태를 추론할 수 있다. 또한 퍼지 기술 로직 언어(Fuzzy Description Logic)기반 추론기인 FiRE를 이용하여 이를 검증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.167-172
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• 2006

As ubiquitous devices are fast spreading, the communication problem between humans and these devices is on the rise. The use of context is important in interactive application such as handhold and ubiquitous computing. Context is not crisp data, so it is necessary to introduce the fuzzy concept. The proxity relation is represented by the degree of closeness or similarity between data objects of a scalar domain. A context manager of context-awareness system evaluates imprecise queries with the proximity relations. in this paper, a systematic proximity elicitation method are proposed. The proposed generation method is simple and systematic. It is based on the well-known fuzzy set theory and applicable to the real world applications because it has tuning parameter and weighting factor. The proposed representations of proximity relation is more efficient than the ordinary matrix representation since it reflects some properties of a proximity relation to save space. We show an experiments of quantitative calculate for the proximity relation. And we analyze the time complexity and the space occupancy of the proposed representation method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1115-1120
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• 2003

The present paper shows the possible development of particle swarm optimization (PSO) based fuzzy-neural networks (FNN) which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs which can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by an user. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. Hidden Markov Model (HMM) based automatic speech recognizers are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system is successfully employed in a real life situation for motion control of a redundant manipulator.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.219-226
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• 2010

Recently most parking control systems provide customers with various services, but most of the services are just the extension of parking spaces, automatic parking control system and so on. It is essential to use the satisfaction degree as the extent that customer are satisfied with parking control system to improve the quality of the system services and diversify the system services. The degree of satisfaction is different from customer to customer in same condition and can be represented as linguistic variables. In this paper, we present therefore a technique that quantify how much customer are satisfied with parking control system and fuzzy inference system architecture as a solution that can help us to make a efficient decision for these parking problems. In this architecture, inference engine using fuzzy logic compares context data with the rules in the fuzzy rule-based system, gets the sub-results, aggregates them and defuzzifies the aggregated result using MATLAB application programming to obtain crisp value. Fuzzy inference system architecture presented in this paper, can be used as a efficient method to analyze the satisfaction degree which is represented as fuzzy linguistic variables by human emotion. And it can be used to improve the satisfaction degree of not only parking system but also other service systems of various domains.