• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• 한국수학사학회지
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• 제14권2호
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• pp.93-100
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• 2001

The problem of vagueness has been investigated for a long time by philosophers and mathematicians. There are there approaches in mathematics to the problem, which are probability theory, fuzzy logic, and rough set theory. In this paper I introduce these theories and their meanings.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.457-458
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• 2013

• 대한안전경영과학회지
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• 제13권4호
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• pp.117-124
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• 2011

Supplier selection is one of the most important activities of a company. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer's company. In addition, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer. Therefore a performance evaluation system for supplier and green suppliers is necessary to determine the suitability of suppliers to cooperate with the company. Supplier and green supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in these problems make the decision making complicated. The objective of this study is to construct a decision-making process using fuzzy analytic hierarchy process (FAHP) and balanced scorecard (BSC) for evaluating supplier and green suppliers in the manufacturing industry. The BSC concept is applied to define the hierarchy with four major perspectives and performance indicators are selected for each perspective. FAHP is then proposed in order to tolerate vagueness and ambiguity of information. Finally, FAHP is applied to facilitate the solving process. With the proposed approach, manufacturers can have a better understanding of the capabilities that supplier and green supplier must possess and can evaluate and select the most suitable supplier for cooperation.

• 한국조경학회지
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• 제43권2호
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• pp.87-104
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• 2015

랜드스케이프 어바니즘이 이룬 성과만큼 담론에 대한 다양한 비판도 함께 제기되어왔다. 오늘날 랜드스케이프 어바니즘의 이론적 구도와 한계를 명확히 파악하기 위해서는 그동안 제기된 비판적 견해에 대한 고찰이 필요하다. 본 연구는 각각의 관점에 따라 제기되어온 여러 비판적 견해를 분석할 수 있는 종합적인 해석의 틀이 필요하다는 판단 하에 논리학과 기호학에서 사용되는 내포와 외연의 개념을 도입하여 분석을 진행한다. 랜드스케이프 어바니즘의 전개과정을 비판적 견해를 중심으로 통시적으로 재구성한 뒤, 이를 바탕으로 30여 편의 비판적 견해가 담긴 문헌을 선정하여 분석한다. 비판적 견해의 성격에 따라 내재적 비판과 외재적 비판으로 구분하고 이중 내재적 비판에 해당되는 견해들만을 본 연구의 주제로 다룬다. 랜드스케이프 어바니즘에 대한 내재적 비판은 이론, 실천, 그리고 이론과 실천과의 관계로 구분할 수 있다. 이론적 내용에 대한 비판은 개념의 모호함과 개념의 모순을 지적하는 비판들로 나누어진다. 이중 개념의 모호함은 사전적 애매함과 내포적 모호함으로 인해 발생한다. 기존의 개념을 확장적으로 재해석하면서 발생한 개념의 모호함의 문제는 담론의 구조적인 한계를 드러낸다. 실천에 대한 비판의 경우 실천적 결과의 부재, 형태중심적 실천, 기존 조경과의 실천적 차별성의 세 가지 유형으로 구분된다. 이중 실천적 결과의 차별성 부재는 다수의 경계사례를 허용하는 외연적 모호함으로 인해 나타나는데, 경계사례들이 기존 조경의 외연이나 경계사례와 중복이 되면서 담론의 정체성이 모호해지는 결과를 초래한다. 이론과 실천의 관계에 대한 비판은 대부분 실천적 방법론에 대한 문제를 제기한다. 이에 대한 비판들은 실천적 방법의 오류와 실천적 방법의 부재를 지적하는 두 가지 유형으로 나뉜다. 이 중 실천적 방법의 부재는 그동안 랜드스케이프 어바니즘이 제시한 해답으로는 해결이 될 수 없는 이론의 구조적인 문제에 해당된다. 담론의 구조적인 문제들은 랜드스케이프 어바니즘의 약점이기도 하지만 이론적인 영역을 확장하고 잠재성을 인정받을 수 있었던 요인으로 작용하기도 한다. 향후 랜드스케이프 어바니즘의 방향을 파악하기 위해서는 내재적 비판뿐만 아니라 외재적 비판에 대한 고찰도 함께 이루어져야 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• 한국건축시공학회지
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• 제4권2호
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• pp.119-128
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• 2004

Lately construction industry tends to prefer remodeling of existing buildings rather than new construction or reconstruction of buildings due to strengthening of several restriction related to real estates along with prolonged depression. And also, remodeling of building costs less and creates less wastes compared to reconstruction and so it is more profitable in financial and environmental view. However remodeling is process of creating new environment with existing building. Therefor remodeling must follow the procedure realizing problem and fix the problem based on through investigation on existing building and users requirement must be faithfully reflected. Specially in case of apartment houses, since vagueness on ownership and management authority on common parts exists. Hereupon, in this study we are to present the procedure of analyzing apartment house remodeling method through user requirement by approaching to several considerable factors in user request side.

• Architectural research
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• 제12권2호
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• pp.85-92
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• 2010

In recent years, construction projects have been forced to cope with lack of skilled labor and increasing hazard circumstance of human operations. A construction robotic system has been frequently accomplished as one alterative for overcoming these difficulties in increasing construction quality, enhancing productivity, and improving safety. However, while the complexity of such a system increases, there are few ways to carry out an assessment of the system. This paper introduces a knowledge-based multi-criteria decision-making process to assist decision makers in systematically evaluating an automated system for a given project and quantifying its system performance index. The model employs linguistic terms and fuzzy numbers in attempts to deal with the vagueness inherent in experts' or decision makers' subjective opinions, considering the contribution resulted from their knowledge on a decision problem. As an illustrative case, the system, called Robotic-based Construction Automation, for constructing steel erection of high-rise buildings was applied into this model. The results show the model's capacities and imply the application to other extended types of construction robotic systems.

• 한국컴퓨터정보학회논문지
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• 제23권8호
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• pp.45-50
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• 2018

Many of the current successful commercial recommender systems utilize collaborative filtering techniques. This technique recommends products to the active user based on product preference history of the neighbor users. Those users with similar preferences to the active user are typically named his/her neighbors. Hence, finding neighbors is critical to performance of the system. Although much effort for developing similarity measures has been devoted in the literature, there leaves a lot to be improved, especially in the aspect of handling subjectivity or vagueness in user preference ratings. This paper addresses this problem and presents a novel similarity measure using fuzzy logic for selecting neighbors. Experimental studies are conducted to reveal that the proposed measure achieved significant performance improvement.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.119-122
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• 2002

The research Interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tiled to model the inherent uncertainty and vagueness of color data using fuzzy color model. By laking a fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with the two inter-color distance measures. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.