• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.118-125
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• 2004

The purpose of this study is to decide an appropriate neighborhood and a transition rule of cellular automata by analyzing the past growth process of urban areas in Gimhae. With cellular automata which can manage the change based on the dynamic model and time, this study analyzes the urban growth of Gimhae from 1987 to 2001. Also, through the simulation of different types for neighborhood and transition rules, we can find the appropriate neighborhood and the transition rule for Gimhae. In conclusion, the forecast of physical urban growth pattern is more accurate under conditions when the number of matrixes for the neighborhood is small, the shape of the neighborhood is rectangular, "${\alpha}$" value, which control the pace of urban growth, is low and the transition possibility ($P_{ij}$) is high.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.413-416
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• 2010

오늘날, 대량의 데이터를 수집, 저장 및 관리하는 데이터베이스 기술의 진보를 기반으로, 의료, 과학, 교육, 비즈니스 등 다양한 분야에서 발생되는 대규모 데이터를 축적하게 되었다. 다양한 분야에서 축적된 대량의 데이터에 내재된 유용한 정보를 수월하게 추출하여 분석하기 위해 널리 사용되고 있는 형식개념분석기법은, 주어진 데이터로부터 정보의 최소단위로써 개념들을 추출하고, 개념들 사이의 관계를 토대로 개념계층구조를 구축하기 위한 정형화된 데이터마이닝 기법을 제공하고 있다. 본 논문에서는, 주어진 퍼지 데이터에 잠재된 유용한 정보를 추출하기 위해, 퍼지 집합 이론을 형식개념분석기법에 접목한 퍼지개념분석기법과 이를 지원하기 위해 본 연구에서 개발된 FFCA-Wizard를 소개한다. 또한, FFCA-Wizard를 사용하여 실세계 데이터를 대상으로 퍼지개념분석을 실시한 실험 결과를 보고한다.

• 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 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.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.111-121
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• 2009

In this paper, we propose a multimedia recommender system using user's playback time. Proposed system collects multimedia content which is requested by user and its user‘s playback time, as web log data. The system predicts playback time.based preference level and related contents from collected transaction database by fuzzy association rule mining. Proposed method has a merit which sorts recommendation list according to preference without user’s custom preference data, and prevents a false preference. As an experimental result, we confirm that proposed system discovers useful rules and applies them to recommender system from a transaction which doesn‘t include custom preferences.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.21-26
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• 2006

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the user's access pattern to web site and their following purchasable items and improves their web page on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning, it employs Apriori algorithm, which is a method that searches the association rule. It reasons the web pages by considering the user's access pattern and time by using the web log and reasons the user's purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of user's web pages and displays the inferred goods on user's web pages.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.2
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• pp.11-21
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• 2000

Tunnelling has been created as a great extent in view of less land space available because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities. In tunnelling, it is often faced that measures are obliged to be taken without confirmation for such abnormality as diverged movement of surrounding rock mass, growing crack of shotcrete and yielding of rockbolts. In this case, it is usually said that the judgments of experienced engineers for the selection of measure are importance and allowed us to get over the situations in many construction sites. But decrease of such experienced engineers need us to develop the new system to assist the selection of measures for the abnormality without any experiences of similar tunnelling sites. In this study, After a lot of tunnelling reinforcement methods were surveyed and the detail application were studied, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.540-549
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• 2003

In collaborative recommendation two models are generally used: the user model and the article model. A user model learns correlation between users preferences and recommends an article based on other users preferences for the article. Similarly, an article model learns correlation between preferences for articles and recommends an article based on the target user's preference for other articles. In this paper, we investigates various combination methods of the user model and the article model for better recommendation performance. They include simple sequential and parallel methods, perceptron, multi-layer perceptron, fuzzy rules, and BKS. We adopt the multi-layer perceptron for training each of the user and article models. The multi-layer perceptron has several advantages over other methods such as the nearest neighbor method and the association rule method. It can learn weights between correlated items and it can handle easily both of symbolic and numeric data. The combined models outperform any of the basic models and our experiments show that the multi-layer perceptron is the most efficient combination method among them.