• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.1-9
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• 2008

Recently, various researches have been studied on the predict method of land change according to its development. The Cellular Automata(CA) is one of the most popular methods in the urban growth modeling. The basis principle of CA is to repeat operations, which convert the current cell into new cell state by the transaction rule. It will minimize the loss of data by using Fuzzy-AHP and it can lead the flexible urban growth modeling. However, AHP would have a disadvantage to repeat the procedure of the collecting intentions until it derives the weight. Also, it is necessary for the simulation of CA to repeat the operations and the test of data accuracy should be accompanied. The purpose of this study is to predict the Busan city growth model and analyze it according to the automated test method by applying CA as well as Fuzzy-AHP. This study shall improve the difficulties caused by complexity and repetitiveness in the urban grow modeling. The practical modeling could be derived from the verification, and the derived modules could be applied to the similar case studies.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.58-68
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• 2006

There are two traditional scheduling methods known as PQ and WRR in the DiffServ network, however, these two scheduling methods have some drawbacks. In this paper, we propose an algorithm that can be adopted in WRR scheduler with making up for weak points of PQ and WRR. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class. To evaluate the performance of the proposed algorithm, We accomplished a computer simulation using NS-2. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.128-131
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• 2006

There are two traditional scheduling methods known as PQ and WRR in the DiffServ network, however, these two scheduling methods have some drawbacks. In this paper, we propose an algorithm that can be adopted in WRR scheduler with making up for weak points of PQ and WRR. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assigns the weight of WRR scheduler with checking the Queue status of each class. To evaluate the performance of the proposed algorithm, We accomplished a computer simulation using NS-2. In result, the proposed algorithm enhances the packet discard rate at the EF class than WRR scheduling method and the AF4 class than PQ scheduling method.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.509-516
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• 2015

Intangibility, measurement difficulty and irreversibility, which are the characteristics of service, have such problems as obscurity and uncertainty in quality evaluation. Technical entrepreneurship education, a sort of public service, also contains such characteristics of service. To objectively evaluate the service quality of technical entrepreneurship education, this paper drew up factors as hierarchical structure, centered on FAHP technique, and conducted pre-processing, inputted those factors into triangular fuzzy number fuzzy judgement matrix, and calculated their weights. In this manner, this paper proposed a TESE system, through which an analysis can be conducted by drawing relative importance and priorities of the factors. The proposed system can efficiently evaluate the qualitative technical start-up education service quality factors quantitatively in the diversely changing technical start-up environment in view of the highest result quality (41%), which means performance in the relative importance of major factors. Namely, this paper confirmed that clear decision making can be made through an experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.91-96
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• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.131-135
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• 2018

The heart disease taking the second place of the cause of the death of modern people is a terrible disease that makes sudden death without noticing. To judge the aortic valve disease of heart diseases a name of disease was diagnosed using psychological data provided from physioNet. Aortic valve is a valve of the area that blood is spilled from left ventricle to aorta. Aortic stenosis of heart troubles is a disease when the valve does not open appropriately in contracting the left ventricle to aorta due to narrowed aortic valve. In this paper, 3126 samples of cardiac sound data were used as an experiment data composed of 180 characteristics including normal people and aortic valve stenosis patients. To diagnose normal and aortic valve stenosis patients, NEWFM was utilized. By using an average method of weight as an feature selection method of NEWFM, the result shows 91.0871% accuracy.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.699-708
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• 2010

Korean universities have selected candidates through admission officer system since 2009. However, the universities now have to settle several problems that they faced at the first stage of the system. Therefore, this research, taking the admission screening data of J University as examples, aims to discuss how differently the weight of the screening factors appears depending on the subjects related to college entrance, such as parents, teachers, and admission officers. The research indicates that the subjects have different perspectives about entrance screening requisites. Parents and teachers more value the student record that is a countable indicator than the letter of self-recommendation that is a uncountable indicator. However, it also indicates that admission officers take attitude against parents and teachers.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.363-371
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• 2016

Technology evaluation for technology innovation of venture firms should take the rapidly changing corporate environment, the ambiguity of language used in evaluation, and the inaccuracy of evaluation index into consideration. In this paper, targeting the absorptive capacity and technological entrepreneurship which are typical evaluation factors of venture firms, an ACTEM model to calculate the importance, priority, and dynamic capability of factors, applying Fuzzy-AHP was proposed. The fuzzy theory was introduced to compensate for the ambiguity of cognitive judgments when calculating weighted values for the factors that made up an assessment scale. An assessment criteria framework for absorptive capacity, technological entrepreneurship, and dynamic capability, which were not considered in previous studies on the evaluation and measurement of technological prowess, so that the users could have a realistic alternative to choose. The study compared the ACTEM model with the old AHP assessment method and found that "knowledge acquisition" and "producing ability" were the highest in absorptive capacity and technological entrepreneurship, respectively, thus demonstrating its validity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1731-1741
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• 1999

Color images are often corrupted by the noise due to noisy sensors or channel transmission errors. Some filters such as vector media and vector $\alpha$-trimmed mean filter have bee used for color noise removal. In this paper, We propose the fuzzy cluster filters based on the possibilistic c-means clustering, because the possibilistic c-means clustering can get robust memberships in noisy environments. Also, we propose weighted vector $\alpha$-trimmed mean filter to improve the conventional vector $\alpha$-trimmed mean filter. In this filter, the central data are more weighted than the outlying data. In this paper, we implemented the color noise generator to evaluate the performance of the proposed filters in the color noise environments. The NCD measure and visual measure by human observer are used for evaluation the performance of the proposed filters. In the experiment, proposed fuzzy cluster filters in the sense of NCD measure gave the best performance over conventional filters in the mixed noise. Simulation results showed that proposed weighted vector $\alpha$-trimmed mean filters better than the conventional vector $\alpha$-trimmed mean filter in any kinds of noise.