• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.172-174
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• 2002

Ethernet PON(Passive Optical Network)은 최근 들어 활발하게 연구되고 있는 경제적이고 효율적인 가입자망 구조이다. 본 논문에서는 OPNET 시뮬레이션 도구를 사용하여 Ethernet PON 시뮬레이션 모델을 구현하고, 시뮬레이션 한다. OLT(Optical Line Termination)에 연결되는 ONU(Optical Network Unit)의 개수, 트래픽 및 네트워크 파라미터에 대한 ONU의 버퍼 크기(buffer size)를 분석하고, 이 결과로서 실제 네트워크에 적용할 효율적이고 적정한 ONU의 버퍼 크기를 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.87.2-87
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• 2002

$\textbullet$ Introduction of an adaptive echoes canceller in telephone network and the propose $\textbullet$ The echoes canceller structure $\textbullet$The Lattice/Transversal Joint structure $\textbullet$ The propose robust variable step-size algorithm for lattice form structure $\textbullet$ Performance evaluation $\textbullet$ Simulation results $\textbullet$ Conclusion

• Journal of Digital Contents Society
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• v.6 no.1
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• pp.7-12
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• 2005

The purpose of this study is to study the relability of degree 2 ICR(Interleaved Cydic Ring) network and to compare with the other rings. Two node reliability is the probability that source node communicates with the destination node through a specified time interval for ICR network. The impact for change of failure rate is studied for ICR network for small size of network, the exact value of reliability is calculated but the approximation of average reliability general function from upper bound and lower bound reliability is obtained for large size of it. The reliability of ICR network is compared with it of the other rings according to changing the cycle value of ICR.

• Journal of Korean Society of Rural Planning
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• v.24 no.3
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• pp.127-135
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• 2018

In countries and regions population plays an important role. Recently the importance of population migration increased as population growth slowed. Researches on population migration are mainly focused on the analysis of the population movement factors and the regional structure analysis using the network analysis method. Analysis of regional structure through population movement is not enough to explain the phenomenon of migration of small cities and rural regions. In this study, to overcome the limit of previous studies the characteristics of the population movement rate according to the size of the population were analyzed. Also network analysis using the population movement OD (Origin and Destination) and population movement rate OD were conducted and the results of them were compared. As the results of analysis by the regional population scale, the population movement by population size showed a big difference in the areas with more than 100 thousand people and less than 100 thousand people. Migration to the outside of the province was the most frequent in regions with 30,000~50,000 people. The population migration rate network analysis result showed that the new area with large population inflow capacity was identified, which could not be found in the population movement network analysis because population movement number is small. The population movement rate irate is expected to be used to identify the central regions of the province and to analyze the difference in resident attractiveness.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.1
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• pp.37-44
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• 2001

This paper describes the position control method in yard crane drive system using Lonworks network, which is a leading industrial control network. The network is composed of host computer and three motor drive systems for both gantry and trolley position controls of both gantry and trolley are controlled with the simulator of yard crane, the size of which is about 1/10 with the real yard crane.

• Information Systems Review
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• v.15 no.2
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• pp.91-110
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• 2013

Due to the intense competition and low switching cost, to find out which factors will significantly impact on user's continuance intention is very important for mobile instant messaging (IM) practitioners. In this study, we adopted network externalities and perceived service quality as independent variables based on the definition of mobile IM service. Network externalities also include direct externalities (referent network size) and indirect externalities (perceived complementarity). The result of this study shows that referent network size has a critical influence on perceived usefulness and perceived complementarity has a critical influence on perceived enjoyment; perceived service quality, as we expected, has significantly impact on not only customer's satisfaction but also perceived usefulness and perceived enjoyment. Meanwhile, both perceived usefulness and perceived enjoyment have directly critical influences on customer's continuance intention.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.547-559
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• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5149-5167
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• 2017

Reducing energy consumption in a wireless video sensor network (WVSN) is a crucial problem because of the high video data volume and severe energy constraints of battery-powered WVSN nodes. In this paper, we present an adaptive dynamic resizing approach for a SRAM communication buffer in a WVSN node in order to reduce the energy consumption and thereby, to maximize the lifetime of the WVSN nodes. To reduce the power consumption of the communication part, which is typically the most energy-consuming component in the WVSN nodes, the radio needs to remain turned off during the data buffer-filling period as well as idle period. As the radio ON/OFF transition incurs extra energy consumption, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, a large-sized SRAM buffer results in more energy consumption because SRAM power consumption is proportional to the memory size. We can dynamically adjust any active buffer memory size by utilizing a power-gating technique to reflect the optimal control on the buffer size. This paper aims at finding the optimal buffer size, based on the trade-off between the respective energy consumption ratios of the communication buffer and the radio part, respectively. We derive a formula showing the relationship between control variables, including active buffer size and total energy consumption, to mathematically determine the optimal buffer size for any given conditions to minimize total energy consumption. Simulation results show that the overall energy reduction, using our approach, is up to 40.48% (26.96% on average) compared to the conventional wireless communication scheme. In addition, the lifetime of the WVSN node has been extended by 22.17% on average, compared to the existing approaches.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.33-43
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• 2002

Various kinds of estimation methods have been developed for imputation of categorical missing data. They include category method, logistic regression, and association rule. In this study, we propose two fusions algorithms based on both neural network and voting scheme that combine the results of individual imputation methods. A Mont-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data pattern are (1) input-output function, (2) data size, (3) noise of input-output function (4) proportion of missing data, and (5) pattern of missing data. Experimental study results indicate the following: when the data size is small and missing data proportion is large, modal category method, association rule, and neural network based fusion have better performances than the other methods. However, when the data size is small and correlation between input and missing output is strong, logistic regression and neural network barred fusion algorithm appear better than the others. When data size is large with low missing data proportion, a large noise, and strong correlation between input and missing output, neural networks based fusion algorithm turns out to be the best choice.