• International Commerce and Information Review
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• v.7 no.4
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• pp.305-322
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• 2005

To meet the demand of Korea's e-trade, global e-trade should be implemented and the global network is absolutely needed for the completion of e-trade. In this regard, this study suggested the concept and effect of global e-trade and analyzed the current status of PAA, ASEM projects which have been processing as pilot projects. Also, it derived tasks for the implementation of global e-trade through SWOT analysis and made up questions to the firms which are participating the pilot projects. Therefore, the four implementation models of global e-trade network are brought up. These models are very similar to each pilot project such as PAA, ASEM, and could be classified by developed and developing countries.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• ETRI Journal
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• v.37 no.2
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• pp.262-272
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• 2015

In particular, for a practical mobile robot team to perform such a task as that of carrying out a search and rescue mission in a disaster area, the network connectivity and localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a Global Positioning System is unavailable. This paper proposes the new collective intelligence network management architecture of multiple mobile robots supporting seamless network connectivity and cooperative localization. The proposed architecture includes a resource manager that makes the robots move around and not disconnect from the network link by considering the strength of the network signal and link quality. The location manager in the architecture supports localizing robots seamlessly by finding the relative locations of the robots as they move from a global outdoor environment to a local indoor position. The proposed schemes assuring network connectivity and localization were validated through numerical simulations and applied to a search and rescue robot team.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.10-13
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• 2001

Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm Neural Network consists of neuron and synapse. Synapse memorize last pattern and study new pattern. When Neural Network learn new pattern, it tend to forget previously learned pattern. This phenomenon is called to catastrophic inference or catastrophic forgetting. To overcome this phenomenon, Neural Network must be modularized. In this paper, we propose Moduled Neural Network. Modular Neural Network consists of two Neural Network. Each Network individually study different pattern and their outputs is finally summed by net function. Sometimes Neural Network don't find global minimum, but find local minimum. To find global minimum we use Genetic Algorithm.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.91-112
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• 2008

The purpose of this study is to investigate the motivating factors determining global logistics network construction and to suggest the most suitable solution for the Korean logistics providers in order to facilitate effectively in global logistics network. The major findings are as follows ; Firstly, 'the factor of network' would be the most essential element for the Korean logistics providers. Secondly, we discovered that among various types of global logistics networks, 'Strategic Alliance' would be the most suitable type for the Korean logistic providers. This study provides the basic information for enhancing global logistics network construction to logistics providers and the Korean government respectively. It also suggests that the implication of Government policy to support global logistics networking business for the Logistics providers should be concentrated on supporting and encouraging M&A among the local logistics partners.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1286-1290
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• 2004

This paper proposes a Distributed Autonomous Robotic System (DARS) based on an Artificial Immune Network (AIN) and a Classifier System (CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: aggregation and dispersion. AIN decides one between these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local. The relation between global and local increases the performance of system. Also, the proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.466-469
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• 1996

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.473-479
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• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.217-218
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• 2021

본 논문에서는 다층 퍼셉트론을 활용하여 League of Legends 게임의 승패를 예측하는 Deep Neural Network 프로그램을 설계하는 방법을 제안한다. 연구 방법으로 한국 서버의 챌린저 리그에서 행해진 약 26000 경기 데이터 셋을 분석하여, 경기 도중 15분 데이터 중 드래곤 처치 수, 챔피언 레벨, 정령, 타워 처치 수가 게임 결과에 유의미한 영향을 끼치는 것을 확인하였다. 모델 설계는 softmax 함수보다 sigmoid 함수를 사용했을 때 더 높은 정확도를 얻을 수 있었다. 실제 LOL의 프로 게임 16경기를 예측한 결과 93.75%의 정확도를 도출했다. 게임 평균시간이 34분인 것을 고려하였을 때, 게임 중반 정도에 게임의 승패를 예측할 수 있음이 증명되었다. 본 논문에서 설계한 이 프로그램은 전 세계 E-sports 프로리그의 승패예측과 프로팀의 유용한 훈련지표로 활용 가능하다고 사료된다.