• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.177-181
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• 1996

Systematic classification system for standardization in telecommunication is essential to the standardization R&D strategy. This paper suggests a new reference model for development of work area and classification scheme related to the telecommunications standardization : Cubic and matrix approach. Standardization Work Areas(SWAs) that are upper level of the reference model are classified by its main role and function reflecting the market trends and user needs. Standardization expertise is lower level scheme, which can be regarded as the different possible layers of standardization to be applied to each one of the SWAs grouped under upper level scheme. A new reference model consists of two planes that are SWAs plane and Standardization layer plane. Finally the reference model for classification of SWAs in telecommunication mapping onto matrix table that row and column are defined by SWAs and standardization layer respectively.

• Wind and Structures
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• v.25 no.4
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• pp.361-379
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• 2017

In view of the importance of the wind-structure interaction for tall and slender structures, an aeroelastic model test of the 610m-high TV tower with a complex and unique structural configuration and appearance carried out successfully. The assembled aeroelastic model of the TV tower with complex shape and structure was designed and made to ensure the similarities of the major natural frequencies and the corresponding mode shapes. The simulation of the atmospheric boundary layer with higher turbulent intensity is presented. Since the displacement and acceleration responses at several measurement sections were directly measured in the wind tunnel test, a multi-mode approach was presented to indirectly estimate the displacement and acceleration responses at arbitrary structural floors based on the measured ones. It can be seen that it is remarkable for the displacement and acceleration responses of the TV tower in the two horizontal directions under wind loads and is small for the dynamic response of the torsional displacement and acceleration.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.44-52
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• 2013

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.23-27
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• 2009

A scalable video coding (SVC) extension to the H.264/AVC standard has been developed by the Joint Video Team (JVT). SVC provides spatial, temporal and quality scalability with high coding efficiency and low complexity. SVC is now developing the extension of the first version including color format scalability. The paper proposes to remove some luminance related header and luminance coefficients when an enhancement layer adds only additional color information to its lower layer. Experimental results shows 0.6 dB PSNR gain on average in coding efficiency compared with an approach using the existing SVC standard.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.155-166
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• 2002

Neural networks have been studied as a popular tool for classification and they are very flexible. Also, they are used for many applications of pattern classification and pattern recognition. This paper focuses on Bayesian approach to feed-forward neural networks with single hidden layer of units with logistic activation. In this model, we are interested in deciding the number of nodes of neural network model with p input units, one hidden layer with m hidden nodes and one output unit in Bayesian setup for fixed m. Here, we use the latent variable into the prior of the coefficient regression, and we introduce the 'sequential step' which is based on the idea of the data augmentation by Tanner and Wong(1787). The MCMC method(Gibbs sampler and Metropolish algorithm) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.132-140
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• 2002

A momentum backpropagation neural network is prepared to carry out real-time dynamics simulations of a passenger car. A full-car model of fifteen degrees of freedom was constructed for vehicle dynamics analysis. Human body dynamics analysis was performed for a male driver(50 percentile Korean adult) restrained by a three point seatbelt system. The trained data using the neural network were obtained using a dynamic solver, ADAMS . The neural network were formed based on the dynamics of the simulator. The optimized hidden layer was obtained by selecting the optimal number of hidden layers. The driving scenario including bump passing and lane changing has been used for the estimation of the proposed neural network. A comparison between the trained data and neural network outputs is found to be satisfactory to show the applicability of the suggested approach.

• Journal of the Korean Ceramic Society
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• v.33 no.2
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• pp.155-162
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• 1996

A Taguchi methodology study of the plasma spraying thermal barrier coating (TBC) layer is presented. The experiment parameters were designed by a L8-style orthogonal arrays approach. A Taguchi analysis was conduc-ted through the results of the coating properties which were affected by plasma spraying parameters. Zirconia (partially stbilized with ytrria: PSZ) was sprayed on TiAl intermetallic compound substrates, The coating layer was characterized by thickness microstructure and porosity using SEM and Image analyzer. The coating quali-ties are discussed with respect to thermal barrier effect thermal cycling test6 and adhesion strength test. An optimum condition of plasma spraying process which are derived from the Taguchi analysis could be found for high quality TBC.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.755-757
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• 2009

We developed a novel patterning method of organic light emitting materials using a laser-inscribed sacrificial layer for fabricating high-resolution pixels in organic light emitting displays (OLEDs). Our patterning process is capable of achieving high spatial resolution of about 10 ${\mu}m$. Moreover, it has no detrimental effect on the electrical properties of organic materials. This patterning approach is expected to be applicable for patterning and integrating a wide range of organic materials for organic electronic and optoelectronic devices.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2895-2897
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• 2000

This paper proposes a new fuzzy-neural algorithm for navigation of a mobile robot with stationary and moving obstacles environment. The proposed algorithm has two-layered hierarchical structure such as a lower layer for collision avoidance and goal approach. and upper layer for adaptive combination of these two algorithms. Some computer simulation results for a mobile robot equipped with ultrasonic range sensors show that the suggested navigation algorithm is very effective in stationary and moving obstacles environment.