• 한국정보통신설비학회:학술대회논문집
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• 2005.08a
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• pp.290-296
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• 2005

In this paper, we describe ubiquitous environments and the trend of convergence that is an evolution path of current telecommunication, and show the concept of broadband convergence network, service feature and evolution path. In order to converge wire and wireless communication, telecommunication and broadcasting, voice and data efficiently, broadband convergence network divides a network into service layer, control layer, transport layer, ubiquitous access and terminal layer. Broadband convergence network will be a network that can provide and control broadband multimedia services with QoS and securityof different and customized level. Then we depict characteristics and types of broadband multimedia service, and describe the characteristic of broadband convergence network. Finally, we show ubiquitous network based on the broadband convergence network to provide ubiquitous service which is a future telecommunication service. We also describe requirements of ubiquitous network such as an intelligent and context based platform, convergence terminals, ubiquitous computing devices, etc., and give various emerging technologies and those applications.

• Journal of Computing Science and Engineering
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• v.4 no.1
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• pp.23-51
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• 2010

Wireless Sensor Networks (WSN) have proved to be useful in applications that involve monitoring of real-time data. There is a wide variety of monitoring applications that can employ Wireless Sensor Network. Characteristics of a WSN, such as topology and scale, depend upon the application, for which it is employed. Security requirements in WSN vary according to the application dependent network characteristics and the characteristics of an application itself. Key management is the most important aspect of security as some other security modules depend on it. We discuss application dependent variations in WSN, corresponding changes in the security requirements of WSN and the applicability of existing key management solutions in each scenario.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1946-1952
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• 1999

A new image compression method to preserve edge characteristics in reconstructed images using an unsupervised learning neural is proposed in this paper. By the unsupervised competitive learning which generalizes previously proposed Centroid Neural Network(CNN) algorithm with the geometric characteristics of edge area and statistical characteristics of image data, more codevectors are allocated in the edge areas to provide the more accurate edges in reconstructed image. Experimental results show that the proposed method gives improved edge in reconstructed images when compared with SOM, Modified SOM and M/R-CNN.

• Korean Management Science Review
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• v.33 no.4
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• pp.33-49
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• 2016

The market of platform business is typically served by a few dominant players, presenting "winner-takes-all" phenomenon. This study aims to find service characteristics leading to the phenomenon. Six different service-characteristics were considered : Same-side network effect, cross-side network effects, entry barrier, multi-homing cost, switching cost, and heterogeneity of preference. To assess the degree of concentration of market share, HHI (Herfindahl-Hirschman Index) is calculated for top three major players. Based on the HHI value, 10 most eminent platform businesses are classified into three different segments and each segment is characterized with key factors. The results from this study provide some insight into the strategic management of platform business.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.441-457
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• 2014

Observed from the recent performance evaluation of clustering schemes in wireless sensor networks, we found that most of them did not consider various sensor characteristics and its application environment. Without considering these, the performance evaluation results are difficult to be trusted because these networks are application-specific. In this paper, for the fair evaluation, we measured several clustering scheme's performance variations in accordance with sensor data pattern, number of sensors per node, density of points of interest (data density) and sensor coverage. According to the experiment result, we can conclude that clustering methods are easily influenced by POI variation. Network lifetime and data accuracy are also slightly influenced by sensor coverage and number of sensors. Therefore, in the case of the clustering scheme that did not consider various conditions, fair evaluation cannot be expected.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.163-172
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• 1996

Recently, it was reported that gaussian distribution and difference of two gaussians (DOG) to have band pass characteristics can be generated by simple iterative processes of the diffusion networks. In this paper, we propose method of improved implementation of a diffusion filter which can reduce total runing time, and operate by simple algorithm in contrast to the latest diffusion filter. We rebuild the diffusion network to a difference network which can generate DOG independently. Different filter characteristics are obtained just by each diffusion process and difference process. Quantitative analysis shows that the center frequency and the selectivity of each filter channel can be varied independently. Also, it would requires smaller amount of hardwares than conventioanl method to build a filter bank.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.345-351
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• 2018

This paper analyzes a resonant inverter to generate plasma. The resonant inverter consists of a full bridge converter, resonant network and reactor to generate a magnetic field for plasma generation. A plasma load has very distinct characteristics compared to conventional loads. The characteristics of plasma load are analyzed through experimental results. This paper presents the study on the resonant network, which was performed in order to determine how to achieve a constant current gain. Another important contribution of this study is the analysis of drop-out phenomenon observed in plasma loads which is responsible for unpredictable shutdown of the plasma generator that requires stable operation. In addition, the design process for the resonant network of a plasma generator is proposed. The validity of this study is verified through simulations and experimental results.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.