• Journal of IKEEE
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• v.12 no.4
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1468-1473
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• 2005

In this paper, I proposed the INTelligent Agent System by Kohonen's Self Organizing Neural Network (INTAS). INTAS creates each user's profile from the information. Based on it, learning community grouping suitable to each individual is automatically executed by using unsupervised learning algorithm. In INTAS, grouping and learning are automatically performed on real time by multiagents, regardless of the number of learners. A new framework has been proposed to generate multiagents, and it is a feature that efficient multiagents can be executed by proposing a new negotiation mode between multiagents..

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.253-262
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• 2008

This paper presents a framework for decentralized control of self-organizing swarm systems based on the artificial potential functions (APFs). The framework explores the benefits by associating agents based on position information to realize complex swarming behaviors. A key development is the introduction of a set of association rules by APFs that effectively deal with a host of swarming issues such as flexible and agile formation. In this scheme, multiple agents in a swarm self-organize to flock and achieve formation control through attractive and repulsive forces among themselves using APFs. In particular, this paper presents an association rule for swarming that requires less movement for each agent and compact formation among agents. Extensive simulations are presented to illustrate the viability of the proposed framework.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.106-111
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• 2008

This paper presents a decentralized coordination for a small-scale mobile robot teams performing a task through cooperation. Robot teams are required to generate and maintain various geometric patterns adapting to an environment and/or a task in many cooperative applications. In particular, all robots must continue to strive toward achieving the team's mission even if some members fail to perform their role. Toward this end, given the number of robots in a team, an effective coordination is investigated for decentralized formation control strategies. Specifically, all members are required first to reach agreement on their coordinate system and have an identifier (ID) for role assignment in a self-organizing way. Then, employing IDs on individual robots within a common coordinate system, a decentralized neighbor-referenced formation control is realized to generate, keep, and switch between different geometric shapes. This approach is verified using an in-house simulator and physical mobile robots. We detail and evaluate the formation control approach, whose common features include self-organization, robustness, and flexibility.

• Ocean and Polar Research
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• v.28 no.4
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• pp.425-438
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• 2006

This study has the purpose of providing a framework for analyzing problems of institutional choice, illustrating the complex configuration of variables that must be addressed when individuals in field settings attempt to fashion rules to improve their individual and joint outcomes. The reason for presenting this complex array of variables (i.e., situational variables) as a framework rather than as a model is precisely because one cannot encompass this degree of complexity within a single model. The results of this study imply that it is important for researchers and government officials to understand that appropriators have a certain amount of ability to transform the status quo rules to the alternative rules.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1883-1884
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• 2008

This paper presents a framework for decentralized control of self-organizing swarm agents based on the artificial potential functions (APFs). The framework explores the benefits by associating agents based on position information to realize complex swarming behaviors. A key development is the introduction of a set of association rules by APFs that effectively deal with a host of swarming issues such as flexible and agile formation. In particular, this paper presents an association rule for swarming that requires less movements for each agent and compact formation among agents. Extensive simulations are presented to illustrate the viability of the proposed framework.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.159-175
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• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.435-440
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• 2001

The development of web-aware knowledge discovery system has received a great deal of attention in recent years. It plays a key-enabling role for competitive businesses in the E-commerce era. One of the challenges in developing web-aware knowledge discovery systems is to integrate and coordinate and coordinate existing standalone or legacy knowledge discovery applications in a seamless manner, so that cost-effective systems can be developed without the need of costly proprietary products. In this paper, we present an approach for developing a framework of web-aware interoperable knowledge discovery system to achieve this purpose. This approach applies RMI and high-level code wrapper of Java distributed object computing to address the issues of interoperability in heterogeneous environments, which includes programming language, platform, and visual object model. The effectiveness of the proposed framework is demonstrated through the integration and extension of the two well-known standalone knowledge discovery tools, SOM_PAK and Nenet. It confirms that a variety of interoperable knowledge discovery systems can be constructed efficiently on the basis of the framework to meet various requirements of knowledge discovery tasks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.