• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.46
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• pp.237-261
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• 2010

Resent East Asian economic growth has lead the increase of global trade volume and global logistics to the level of full-scale development as aiming to have quantitative growth in world trades in one hand and increasing mutual dependence among the East Asian Economic Bloc on the other. In other words, East Asian economic current growth is going along as connecting with 2 different process that are the increase of global trade volume and the technical development of global logistics system. And mutual dependence among the East Asian Economic Bloc shows the deepening trend in production and consumption. While having the flow of expansion in East Asian trade volume and logistics network in mind, this paper will study on trend in East Asian intra-trade and then study on change & tasks in the East Asian Logistics system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.22-25
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• 2020

본 논문에서는 큰 크기의 심층 신경망을 압축하기위해 네트워크 수준의 가중치 공유방법인 Global Weight 패러다임을 최초로 제시한다. 기존의 가중치 공유방법은 계층별로 가중치를 공유하는 것이 대부분이었다. Global Weight 는 기존 방법과 달리 전체 네트워크에서 가중치를 공유하는 효율적인 방법이다. 우리는 Global Weight 를 사용하여 학습되는 새로운 컨볼루션 연산인 Global Weight Convolution(GWConv)연산과 GWConv를 적용한 Global Weight Networks(GWNet)을 제안한다. CIFAR10 데이터셋에서 실험한 결과 2.18 배 압축에서 85.64%, 3.41 배 압축에서 85.46%의 정확도를 보였다. Global Weight 패러다임은 가중치 공유가 궁극적으로 풀고자 했던 중복되는 가중치를 최소화하는 획기적인 방법이며, 추후 심도 있는 연구가 수행될 수 있음을 시사한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.3
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• pp.135-149
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• 2008

Traditional attack detection schemes based on packets or flows have very high computational complexity. And, network based anomaly detection schemes can reduce the complexity, but they have a limitation to figure out the pattern of the distributed global scale network attack. In this paper, we propose an efficient and fast method for detecting distributed global-scale network attack symptoms in high-speed backbone networks. The proposed method is implemented at the aggregate traffic level. So, our proposed scheme has much lower computational complexity, and is implemented in very high-speed backbone networks. In addition, the proposed method can detect attack patterns, such as attacks in which the target is a certain host or the backbone infrastructure itself, via collaboration of edge routers on the backbone network. The effectiveness of the proposed method are demonstrated via simulation.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.37-50
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• 2009

In this paper, we proposed a mechanism for effective cooperation in sensor network. There are a few mechanism like RBS, TPSN, FPSN for sensor network. However these are supporting synchronization among nodes without global state. Therefore, we proposed SGSM(Simple Global State Management) to maintain global state among sensor nodes. As experimental results, we confirmed loss rate is within 1% as maintaining global state with SGSM mechanism. In this paper, we defined global state in sensor network and introduced SGSM for improving timming accuracy in sensor environment.

• 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.

• Journal of Distribution Research
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• v.1 no.2
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• pp.141-172
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• 1996

This study is to investigate global logistics strategies of Korean electronics companies. in terms of service parts. According to the survey, Korean electronics companies were pursuing multi-level global logistics network such as global, continental, national, regional and service center echelon for improving the efficiency of service parts global logistics. Especially there was a tendency of developing new continental distribution centers. And the companies were exploiting efficiency through inventory control by multi-level echelon, demand forecasting by the product life cycle and supply and service lead time management. But there were some insufficient factors for the efficiency of global logistics operation at the construction of worldwide real time logistics information system and pursuit of the efficiency on the whole network including subcontractors. For the future competitive advantage of the Korean electronics companies, the following are suggested: 1) the establishment of arranged logistics channel adjusting each company's global strategy, 2) the construction of worldwide real time logistics information system, 3) the reengineering of all logistics procedures such as order processing, shipping, inventory control, etc., 4) the enhancement of the ratio of the supply from the external companies and the internal manufacturing subsidiaries at each continent, 5) and the pursuit of operational internalization of external subcontractors.

• 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.

• 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.

• Bulletin of the Korean Mathematical Society
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• v.49 no.6
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• pp.1193-1198
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• 2012

By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability of the Cohen-Grossberg neural network models. The condition contains and improves some of the previous results in the earlier references.

• 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.