• Journal of Information Technology Services
• /
• v.11 no.3
• /
• pp.1-16
• /
• 2012

This paper is aimed to present a fuzzy decision-making approach to deal with disaster recovery priority decision problem in information system. We derive an evaluation approach based on TOPSIS(Technique for Order Performance by Similarity to Ideal Solution), to help disaster recovery priority decision of total information system for port logistics in a fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by trapezoidal fuzzy numbers. This study applies the fuzzy multi-criteria decision-making method to determine the importance weight of evaluation criteria and to synthesize the ratings of candidate disaster recovery system. Aggregated the evaluators' attitude toward preference, then TOPSIS is employed to obtain a crisp overall performance value for each alternative to make a final decision. This approach is demonstrated with a real case study involving 4 evaluation criteria(system dependence, RTO, loss, alternative business support), 7 information systems for port logistics assessed by 5 evaluators from Maritime Affairs and Port Office.

• International journal of advanced smart convergence
• /
• v.9 no.3
• /
• pp.42-48
• /
• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.262-267
• /
• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.3
• /
• pp.171-177
• /
• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• Korean Journal of Cognitive Science
• /
• v.2 no.2
• /
• pp.309-339
• /
• 1990

The most common way that people communicate is by speaking or writing natural languages.But if people use computers in the modern technology,they should learn artificial programming languages.If computers could understand what people mean when people speak or type natural languages,people would use the computers more easily and naturally.but there is a problem.The language which people use has vagueness.For example,the convential computer system cant's handle the subjective feeling like 'tall' or 'young'.So peole must specify the exact threshold like 'more'than 25 ages'.We have developed the knowledge-based natural language question answering system which can handle sentences having fuzzy concepts by using blackboard model.Our goal of this research is to develop a portable question answering system as interface for database systems or understanding systems.

• The KIPS Transactions:PartB
• /
• v.9B no.2
• /
• pp.163-172
• /
• 2002

One of the characteristics the agent-based negotiation model has is that we have to input the related parameters together. Considering such properties as vagueness, incompleteness and dynamism B2B domain inherently has this may be an unreasonable request. In this paper we suggested a user interface model for B2B negotiation which mainly focussed on this problem. The suggested model supports such functions as a two dimensional negotiation space, diverse negotiation modes and emotion-based control mechanism. In this paper, we tried to show how these three functions can be used for improving the usefulness of the existing agent-based negotiation model.

• Korean journal of communication and information
• /
• v.16
• /
• pp.108-135
• /
• 2001

This study examines the structure of the problems of Korean newspaper reporting raised by the civil society. Based on the differences in the mode of presentation, the problems of newspaper reporting were classified into three types; prominent, associated, and isolate problem. The relations among these problems were defined as the degree of co-occurrence in the monitoring articles. A network analysis was conducted to examine the structure of the problems in Korean newspaper reporting. A $33{\times}33$ similarity matrix of the problems was created by analyzing the data from one monitoring report and two weekly media critics newspapers. The results showed that of the 33 problems, most of them were mentioned as prominent or associated problems. The problems related to the news contents were mostly treated as prominent ones, while the problems about the news writing and reporting styles were frequently treated as associated ones. The network structure of newspaper criticism based on the co-occurrence of 33 problems were divided into six clusters. Among them, the most central was the group including the problems of "unfairness,' "biased interpretation," and "vagueness". In addition, the problems were found to have different roles in the network. Of the 33 problems, two were liaisons and four were attached isolates, indicating the hierarchical structure of problems in newspaper reporting. These results were discussed in the light of two viewpoints; the absence of objective criteria in media criticism, and the need for the comparisons of the current study's results with the opinions of newspaper readers and journalists. finally, the implications for the future studies were presented.

• Environmental and Resource Economics Review
• /
• v.17 no.1
• /
• pp.147-164
• /
• 2008

When measuring the value of environmental attributes of housing, the conventional Hedonic Pricing Method assumes market equilibrium. Thus each attribute is believed to be implicitly valued based on the market price. The revealed preference is the basic logic in this approach. However, if the participants in the housing market are not perfectly informed or feel vagueness regarding the attributes of the housing, the conventional Hedonic Pricing Approach could not provide relevant value of the attribute in question. A Fuzzy Regression Method is suggested to handle with the lack of information or preference uncertainty problem m the Hedonic Pricing Approach. In this paper, our main concern IS given to the fuzziness effect on the airport noise in the metropolitan areas of South Korea.

• Journal of the Korea Safety Management & Science
• /
• v.13 no.3
• /
• pp.63-69
• /
• 2011

As environmental protection is becoming more and more important, green production has become a key issue for almost every manufacturer and will determine a manufacturer can be sustainable in the long term. Therefore a performance evaluation system for green suppliers is necessary to determine the suitability of suppliers to cooperate with the company. While the works on the evaluation and/or selection of suppliers are abundant, those that concern environmental issues are rather limited. The objective of this study is to construct a combined model based on fuzzy analytic hierarchy process (FAHP) and balanced scorecard (ESC) for evaluating green suppliers in the manufacturing industry. The ESC concept is applied to define the hierarchy with four major perspectives (i.e. financial, customer, internal business process, and learning and growth), and performance indicators are selected for each perspective. FAHP is then proposed in order to tolerate vagueness and ambiguity of information. Finally, FAHP is finally constructed to facilitate the solving process. With the proposed model, manufacturers can have a better understanding of the capabilities that a green supplier must possess and can evaluate and select the most suitable green supplier for cooperation.

• Journal of Korea Multimedia Society
• /
• v.11 no.4
• /
• pp.462-470
• /
• 2008

This study applies fuzzy functions to improve image quality under the assumption that uncertainty of image information due to low contrast is based on vagueness and ambiguity of the brightness pixel values. To solve the problem of low contrast images whose brightness distribution is inclined, we use the k-means algorithm as a parameter of the fuzzy function, through which automatic critical points can be found to differentiate objects from background and contrast between bright and dark points can be improved. The fuzzy function is presented at the three main stages presented to improve image quality: fuzzification, contrast enhancement and defuzzification. To measure improved image quality, we present the fuzzy index and entropy index and in comparison with those of histogram equalization technique, it shows outstanding performance.