• 대한안전경영과학회지
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• 제13권3호
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• pp.175-182
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• 2011

The purpose of this article is to suggest what is the desirable direction of economic relationship between Korea and China. The economic relationship between countries is based on how the present network is. As the economic relationship between countries grows, the network between countries will expand. In the past, the economic relationship between Korea and China is cooperative one from the viewpoint of international division of labor. Korean industries was focused on the value-added and mid-advanced technology products, while Chinese was focused on the labor-intensive products. As the China's economy grows for more than thirty years, there is a great change in China's economic policies and environment. China's industry structure is moving from the labor-intensive industry to technology-oriented industry. China's exports to the global market is increasing very fast, and China's domestic market is also growing. The change in Chinese industries' structure bring about severe competition in the global market. The expanding China's domestic market is also good opportunity as the new market in the world. The change in China's industrial structure needs for Korea to establish the 'New Network" between two countries. Korea has to grab the new opportunities in the China's domestic market and find new cooperative network with the products and industries.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 한국기계기술학회지
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• 제20권6호
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• pp.776-782
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• 2018

After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

• 제어로봇시스템학회논문지
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• 제11권6호
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• pp.524-529
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• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• 한국통신학회논문지
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• 제41권12호
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• pp.1959-1968
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• 2016

최근의 자동차 내부에는 수많은 ECU(Electronic Control Unit)들이 사용되고 있으며 또한 각각의 ECU들은 그 특성에 맞게 다양한 종류의 네트워크들에 연결되어 사용되고 있다. 따라서 다양한 네트워크 간의 효율적인 데이터 교환이 매우 중요한 요소로 등장했는데 이러한 서로 다른 네트워크간의 데이터를 교환해 주는 기능을 담당하는 ECU가 게이트웨이이다. 본 논문은 이러한 게이트웨이 설계에 있어서 서로 다른 네트워크 간의 데이터 교환의 효율성을 높이기 위한 매핑 테이블의 구조 및 출력되는 데이터들의 우선순위를 임의로 조절하는 기능을 갖는 새로운 게이트웨이 알고리즘의 구조를 제안하는데 그 목적이 있다. 또한 제안된 게이트웨이 구조는 특정 네트워크에서의 데이터 입력이 복수개의 서로 다른 네트워크로 동시에 변환 및 전달이 가능하고 전체 데이터 구조가 변경되더라도 게이트웨이 내부의 테이블 만을 변경하면 쉽게 적용되는 장점을 가지고 있다.

• E2M - 전기 전자와 첨단 소재
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• 제10권7호
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• pp.651-658
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• 1997

In this paper, effect of interpenetrating polymer network(IPN) introduction on the dielectric properties, heat proof properties, internal structure and defects of the Epoxy/SiO$_2$composite materials, were investigated. we reported a relation between network structures and electrical properties, especially dielectric characteristics with variation of network structures for epoxy composite materials. According to experimental results, the specimens which have single network structures have lower dielectric constant than interpenetrating polymer network(IPN) specimens, but have relatively larger dependency to variation of temperature and frequency. It was confirmed that change of structures is attained by introducing of IPN to insulating materials. Therefore it is counted that introduction of multiple structure including IPN is necessary to improve heat proof and electrical properties.

• Journal of Mechanical Science and Technology
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• 제18권11호
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• pp.1916-1922
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• 2004

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.243-246
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• 2003

A radical environmental change of enterprise and varieties of customer needs demand that the value-added activities of enterprise should be restructured and coordinated. But it is not sufficient that the reengineering processes are restricted withen a firm, so the value-added processes should be expanded into intercompany. The integrated organizational structure between enterprises refer to supply chain network. In this paper we present characteristics, structure concepts, and axiomatic model of supply chain network.

• 대한공간정보학회지
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• 제3권2호
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• pp.151-158
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• 1995

Change detection기법은 영상에서 차이를 알아내기 위하여 가장 많이 사용되는 방법이며 다양한 영상환경에 사용된다. 수치지형모델과 수치영상은 같은 수치격자데이타 구조를 가지므로 Change detection기법을 수치지형모델에 적용할 측량결과의 표고데이타를 불규칙삼각형(TIN)으로부터 격자구조로 변환하고 수치지형모델화하여 구조물의 변형지점을 찾는데 사용함으로 많은 소모성 자재와 인력을 줄일 수 있었다. 그 결과를 가시화하여 건물의 변형이 발생한 지점과 변형향을 수치적으로 나타낼 수 있었다.