• Journal of the Korea Safety Management & Science
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• v.13 no.3
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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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• 2003.10a
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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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.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1959-1968
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• 2016

The recent automobiles, a number of ECU inside the vehicle has been used. Also, each ECU is connected to different types of networks in accordance with the characteristics. Therefore, efficient data exchange between discrete network has emerged as a very important element. The gateway is responsible for the ability to exchange data between discrete network. In this study, we propose the new gateway algorithm to provide the structure of the mapping table to improve the efficiency of data exchange between discrete network. Also it provides a structure of a new gateway algorithm with a function of adjusting the priority of the data to be transmitted to another network arbitrarily. Moreover, the proposed gateway structure may simultaneously convert the transmission data input from a particular network to multiple networks. Another advantage is easy to change the entire data structure only if we change the table structure in the gateway.

• Electrical & Electronic Materials
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• v.10 no.7
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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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• v.18 no.11
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.151-158
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• 1995

Change detection, image differencing technique, is the most widely used in a variety of image environments. The digital terrain model and digital images have the same data structure. This study applied digital terrain model and change detection technique for inspecting the deflection of the structure. Authors make digital terrain model from triangular irregular network(TIN) by leveling data and suggest to possibility recognize modification part and volumes by digital terrain model and change detection technique. Authors can reduce testing materials and man power, and displayed his modification part.