• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.116-126
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• 2006

The purpose of this study is to analyze the growing course of Busan, Gimhae and Jinhae and further find patterns of the urban growth. This study shows that patterns of the urban growth differ from city to city, being influenced by the city's characteristics. Acknowledging this fact would help the decision maker to determine the developing plan of the urban. The methodology for this study is as follows; Fuzzy set concept is applied to minimize the data loss. At the same time, the AHP is used to give a relative weight to each factor. In order to be able to manage the change based on the dynamic model and time, Cellular Automata is introduced to simulate the growth of urban. The results show that the pattern of Gimhae's and Jinhae's growth is the same, whereas that of Busan is different from them. That is to say, each city has regional characteristics. And the pattern of the urban growth is influenced by the regional conditions of the city.

• Journal of Navigation and Port Research
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• v.38 no.1
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• pp.59-64
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• 2014

As global warming continues, the rate of ice melting in polar regions is increasing rapidly. The interest related to north polar route is increasing among not only countries near Arctic ocean but also the other countries, In the past, the classification society rule related to a design and operation of ship operating in polar area has been primarily amended by Russia, Norway and Finland located near Artic area. However recently International Maritime Organization decided to legislate the Polar Code to ensure safety of a ship operating in Arctic and Antarctic Ocean, and it is scheduled to be completed until 2014. The present paper focuses on the survey of the current enactment trends of Polar Code and suggests the confrontational strategy in related organization.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.43-50
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• 2005

This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.353-362
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• 2016

By Wikipedia, data mining is the process of discovering patterns in a big data set involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. and database systems. Association rule is a method for discovering interesting relations between items in large transactions by interestingness measures. Association rule interestingness measures play a major role within a knowledge discovery process in databases, and have been developed by many researchers. Among them, the Hellinger measure is a good association threshold considering the information content and the generality of a rule. But it has the drawback that it can not determine the direction of the association. In this paper we proposed a signed Hellinger measure to be able to interpret operationally, and we checked three conditions of association threshold. Furthermore, we investigated some aspects through a few examples. The results showed that the signed Hellinger measure was better than the Hellinger measure because the signed one was able to estimate the right direction of association.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.69-78
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• 2015

Recently, research on the multi-agent system is developed actively in the various fields, especially on the control of complex system and optimization. In this study, we develop a multi-agent system for NPC simulation in game environment. The purpose of the development is to support quick and precise decision by inferencing the situation of the dynamic discrete domain, and to support an optimization process of the agent system. Our approach employed Petri-net as a basic agent model to simplify structure of the system, and used fuzzy inference engine to support decision making in various situation. Our experimentation describes situation of the virtual battlefield between the NPCs, which are divided two groups, such as fuzzy rule based agent and automata based agent. We calculate the percentage of winning and survival rate from the several simulations, and the result describes that the fuzzy rule based agent showed better performance than the automata based agent.

• Information Systems Review
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• v.7 no.2
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• pp.23-40
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• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.722-727
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• 2009

The technology of distinguishing human motion states is required in the areas of measuring and analyzing biosignals changing according to physical activities, diagnosing sleep disorder, screening the effect of treatment, examining chronic patients' kinetic state, prescribing exercise therapy, etc. The present study implemented a fuzzy inference system based on fuzzy rules that distinguish human motion states (tying, sitting, walking, and running) by acquiring and processing data of LAA, TAA, L-MAD, and T-MAD using ADXL202AE of Analog Devices embedded in an armband. The membership degree and fuzzy rules in each area of input (LAA, TAA, L-MAD, and T-MAD) and output (tying, sitting, walking, and running) data used here were determined using numeric data obtained from experiment. In the results of analyzing data for simulation generated in order of tying$\rightarrow$walking$\rightarrow$running$\rightarrow$tying, the sorting rate for motion states tying, sitting, walking, and running was 100% for each motion.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.114-123
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• 2007

The increasing use of computed tomography (CT) as a diagnostic tool creates the need from and efficient means of evaluating the performance of the CT scanner now in use. Accordingly, acceptance testing and quality assurance of CT is of great importance. The aim of this study is to analyze of AAPM CT performance phantom in the CT accreditation program. The modular phantom offers the CT system with which to measure eight performance parameters. The parameters are listed of CT attenuation coefficient of water, noise, uniformity, spatial resolution, contrast resolution, slice thickness (5 and 10 mm), artifacts and alignment. The phantom evaluation was done by two radiologists. The acceptance testing protocol described here in demonstrates the successful of the guidelines for the quality assurance using AAPM CT performance phantom. We need to be upgraded for the CT image quality and make the standard reference of the quality assurance in the CT.