• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.165-170
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• 2011

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

• Journal for History of Mathematics
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• v.25 no.1
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• pp.1-13
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• 2012

Possibility theory is a kind of mathematics of uncertainty for handling incomplete information. In this paper, we discuss vagueness and randomness as some causes of uncertainty and we introduce the possibility theory as a way of dealing with uncertainty, comparing it with the probability theory.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.148-153
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.995-1000
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• 2003

This paper proposes the pedestrian navigation system which deals with subjective information. This system consists of the route setting part and the instruction generation part. The route setting part chooses the route with highest subjective satisfaction degree. The instruction generation part gives users the instructions based on the users' sensuous feeling of distance with linguistic expressions. Fuzzy measures and integrals are applied to the calculation of the satisfaction degree of the route which reflects the users' taste for routes. The instruction generation part has database of users' cognitive distance. Users' cognitive distances are expressed by fuzzy sets that correspond to linguistic terms. The system generates the instructions with linguistic terms which have the highest fitness value for the users' sensuous feeling of distance. This paper also performs subjective experiments in order to confirm the validity of the present system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.461-466
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• 1993

This paper focuses on development and implementation of a performance management algorithm for IEEE802.4 token bus networks to serve large-sale integrated systems. The delivery of time critical messages within delay constraints is an important criterion in the design and management of computer communication networks. In order to achieve this goal, the theory of fuzzy sets has been employed to imitate human's reasoning. The Fuzzy Network Performance Manager(FNPM) is composed of two parts: FNPM1 & FNPM2. FNPM1 is solily intended to satisfy the data latencyfor the highest priority while the other part is trying to satisfy those for the lower priorities. The FNPM requires average data latency, throughput, and token circulation time for its inputs. The efficacy of the FNPM has been evaluated by a series of simulation experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.71-76
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• Computational Structural Engineering
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• v.11 no.4
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• pp.219-227
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• 1998

실제 구조물에 있어 확률, 통계 및 이론으로 구해진 랜덤성을 갖는 객관적 불확실성뿐만 아니라 설계자의 경험이나 공학적 판단에 의해 주관적으로 평가되는 인간오차나 시공중의 과오 또는 구조설계에 미치는 사회적, 정치적 및 경제적 요청 등의 퍼지성을 갖는 주관적 불확실성이 존재하기 때문에 현실적으로 랜덤성과 퍼지성을 동시에 고려한 실뢰성평가 즉, 안전성평가에 대한 퍼지이론의 도입이 필수 불가결하다. 따라서 본 연구에서는 기존 구조물의 객관적·주관적 불확실성을 동시에 고려한 신뢰성해석방법으로 베이즈의 의사결정이론에 퍼지이론을 병합한 퍼지-베이즈 신뢰성해석 알고리즘을 개발하여 건축구조물의 신뢰성평가 및 안전성평가에 적용하여 분석하였다.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.47-60
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• 1996

Nitrate contamination problems from groundwater supplies have been reported throughout many countries in the world, including Korea. Nitrate salts can induce methemoglobinemia and possibly human gastric cancer. To reduce human health risk from nitrate in groundwater supplies, several nitrate risk-management strategies can be developed based on the acceptable level of human health risk, the reasonableness of nitrate-control cost, and the technical feasibility of nitrate-control methods. However, due to a lack of available information, assessing risk, cost and technical feasibility contains elements of uncertainty. In the present paper, a nitrate risk-management methodology using fuzzy sets in combination with a multiobjective decision-making (MODM) technique is developed to assist decision makers in evaluating, with uncertain information, various nitrate risk-management strategies in order to decide a proper strategy.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.45-62
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• 2004

Any business activities may have impact on environment to a certain extent. Enterprises must find appropriate approaches to measure the impact on these environmental aspects, which can be used as the basis to direct enterprises' efforts to improve the environmental impact. The method used to evaluate significant factors in life cycle assessment standards is the one most commonly used by enterprises in general to measure environmental impact. By this method, the decisive factors of each environmental aspect are given scores according to the preset scoring standard of the organization. The scores are added up for each aspect and ranked to assess major environmental aspects. The drawback of this assessment method, that is, it ignores the degree to which each of these factors affects the environment, results in poor credibility. Therefore, this study attempts to solve some qualitative problems by applying to fuzzy theory, in particular, by identifying appropriate fuzzy numbers through fuzzy sets and membership function. Moreover, the study seeks to obtain a crisp value in the process of defuzzifization in order to make up for the shortfall of the original method in dealing with relative weight of decisive factors and thus increase its applicability and credibility. The department of light production of an electronics company is used as an example in this study to measure environmental aspects by employing both the traditional significant factor method and the fuzzy environmental impact assessment model proposed in this study. Based on verification and comparison of results, the model proposed in this study is more feasible as it reduces partiality in decision-making by taking the relative weights of decisive factors into consideration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.21-31
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• 2002

This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.