• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.23-28
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• 2011

Ultrasound images for the abdominal muscles are complicated enough to have difficulty in interpreting their results. For better interpretation, magnifying the original image is necessary but its magnified image could be deteriorated and suffer from information loss. Thus, in this paper, we propose a magnifying method that reduces the gap between the original image and the magnified one in quality using a fuzzy method with weights for its brightness and interpolation. The proposed method extracts information of pixels in magnified image that have most similar characteristics of the original one by applying fuzzy membership function. In the process, the difference in the brightness between pixels of the magnified image and the original one using bilinear interpolation method and the weight value using the interpolation from multiplied values of four pixels are supplied to the fuzzy membership function. In this experiment, the proposed method reduces the cloudy phenomenon appears commonly compared to the bilinear interpolation method among those qualitative issues of image interpretation.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.439-447
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• 2011

It is a main concern for sustainable development in water resources management to evaluate adaptation capability of water resources structures under the future climate conditions. This study introduced the Fuzzy Inference System (FIS) to represent the change of release and storage of reservoirs in the Han River basin corresponding to various inflows. Defining the adaptation capability of reservoirs as the change of maximum and/or minimum of storage corresponding to the change of inflow, the study showed that Gangdong Dam has the worst adaptation capability on the variation of inflow, while Soyanggang Dam has the best capability. This study also constructed an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the more accurate and efficient simulation of the adaptation capability of the Soyanggang Dam. Nine Inflow scenarios were generated using historical data from frequency analysis and synthetic data from two general circulation models with different climate change scenarios. The ANFIS showed significantly different consequences of the release and reservoir storage upon inflow scenarios of Soyanggang Dam, whilst it provides stable reservoir operations despite the variability of rainfall pattern.