• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1521-1528
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• 2010

This paper examines technical writing and teaching methods based on the perspectives of the complex learning system theory. So, the paper first discusses the constituent elements and characteristics of the complex learning system theory and continues to examine the potential of applying the complex learning system theory to new teaching methods. As a result, not only did the research expand the approach methods of providing technical writing education but also confirmed the potential of actual implementation. Such results will provide a leeway to start applying new teaching methods for technical writing education. Furthermore, the paper proposes more detailed case studies related to this topic as well as development of this research to produce textbooks and other higher level researches.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.82-93
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• 2016

The latest development of the Korean drama production industry has been accompanied by the steep rise in the number of players and the contraction of the advertizing market, which have combined to intensify the competition severely. Then, China happened. Entertainment agencies' logical strategic choice to maximize production revenue potential was to secure A-list actors who can sell, pushing up their prices, that process has continued to even compromise the health of the production industry as well as the agencies' financial integrity. The drama production industry, including entertainment agencies, should shift the strategic focus from simple production revenue generation to profit maximization through diversifying revenue sources like securing IP rights.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.713-718
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• 2003

The CONDENSATION (Conditional Density Propagation) algorithm has a robust tracking performance and suitability for real-time implementation. However, the CONDENSATION tracker has some difficulties with real-time implementation for multiple people tracking since it requires very complicated shape modeling and a large number of samples for precise tracking performance. Further, it shows a poor tracking performance in the case of close or partially occluded people. To overcome these difficulties, we present three improvements: First, we construct effective templates of people´s shapes using the SOM (Self-Organizing Map). Second, we take the discrete HMM (Hidden Markov Modeling) for an accurate dynamical model of the people´s shape transition. Third, we use the competition rule to separate close or partially occluded people effectively. Simulation results shows that the proposed CONDENSATION algorithm can achieve robust and real-time tracking in the image sequences of a crowd of people.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.411-426
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• 2009

Most of the metaheuristics are made by imitating the action of the animals. In this paper, we proposed Mine Algorithm. The Mine Algorithm is a metaheuristic that imitates the action of the human being. Speaking of search, the field in which the know-how and the heuristics of the human being are melted best is the mining industry. In the Mine Algorithm we formalize the action pattern of the human being by focusing the mine business. The Mine Algorithm uses various searching techniques fluently and shows equally good performance for broad problems. That is, it has good generality. We show the improved generality of the Mine Algorithm by the comparing experiments with the conventional metaheuristics.

• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.114-130
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• 2006

This paper deals with complexity theory to describe amphibious warfare situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. EINSTein model is an agent-based artificial "laboratory" for exploring self-organized emergent behavior in land combat. Many studies have shown that existing Lanchester equations used in most war simulation models does not describe changes of combat. Future warfare will be information warfare with various weapon system and complex combat units. We have compared and tested combat results with Lanchester models and EINSTein model. Furthermore, the EINSTein model has been applied and analyzed to amphibious warfare model such as amphibious assault and amphibious sudden attack. The results show that the EINSTein model has a possibility to apply and analyze amphibious warfare more properly than Lanchester models.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.13-21
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• 1995

In this paper, we improve an image classifier algorithm based on neural network learning. It consists of two steps. The first is input pattern generation and the second, the global neural network implementation using an improved back-propagation algorithm. The feature vector for pattern recognition consists of the codebook data obtained from self-organization feature map learning. It decreases the input neuron number as well as the computational cost. The global neural network algorithm which is used in classifier inserts a control part and an address memory part to the back-propagation algorithm to control weights and unit-offsets. The simulation results show that it does not fall into the local minima and can implement easily the large-scale neural network. And it decreases largely the learning time.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.298-302
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• 2006

As the age of information technology is coming, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. we focused on data mining system by using genetic programming. But, we don't have enough data to perform the learning process of genetic programming. We have to reduce input parameter(s) or increase number of learning or training data. In order to do this, the enhanced data mining system by using GP combined with SOM(Self organizing map) is adopted in this paper. We can reduce the number of learning data by adopting SOM.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.

• The Journal of Information Technology
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• v.7 no.3
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• pp.11-18
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• 2004

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Self-Organization Neural Networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to classification of strokes which is the reference handwritten character. The result shows improved classification rate about 1.44~3.65% proposed method compare with Kohonan and Mao's algorithms, in this paper.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.360-367
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• 2002

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells. The purpose of this study is to develop a two-phase machine-part group formation algorithm based on Self-Organizing Maps (SOM). In phase I, it forms machine cells from the machine-part incidence matrix by means of SOM whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the input vectors. In phase II, it generates part families which are assigned to machine cells by means of machine ratio related with processing part and it gives machine-part group formation. The proposed algorithm performs remarkably well in comparison with many well-known algorithms for the machine-part group formation problems.