• ETRI Journal
• /
• v.26 no.5
• /
• pp.505-508
• /
• 2004

We propose a new arbitration method for an input buffered switch with a buffered crossbar. In the proposed method, an exhaustive polling method is used to decrease the synchronization. Using an approximate analysis, we explain how the proposed method improves the switch performance. Also, using computer simulations, we show the proposed method outperforms the previous methods under burst traffic.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.5
• /
• pp.650-657
• /
• 1999

In this paper, a real-time communication method using a PICNET-NP(Plant Instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS(Distributed Control System) in unclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.797-801
• /
• 2003

In this paper, a real-time communication method using a PICNET-NP (Plant Instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS (Distributed Control System) in nuclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.12
• /
• pp.2475-2482
• /
• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is compensated using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could compensated the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for compensation of the error due to hole eccentricity in hole-drilling method.

• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.412-418
• /
• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is corrected using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could corrected the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for correction of the error due to hole eccentricity in hole-drilling method.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.4
• /
• pp.156-162
• /
• 2006

Transmission zones can be defined based on LMPs. Each zone consists of nodes with similar LMPs, and zonal price is determined by average nodal prices in each zone.[1] Network reduction is still important for the analysis of zonal systems under electricity market environments, even though the computing capability of computer system can deal with entire power systems. The Similarity Index is a good performance measure for the network reduction.[2] It can be applied to the network reduction between zones categorized by the nodal prices. This paper deals with a novel network reduction method between zones based on the similarity Index. Line admittances of reduced network were determined by using the least square method. The proposed method was verified by IEEE 39 bus test system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.178-182
• /
• 2007

In this study, we evaluated current raingauge network of Soyanggang dam region applying spatial-correlation analysis and Entropy theory to recommend an optimized raingauge network. In the process of analysis, correlation distance of raingauge stations is estimated and evaluated via spatial-correlation method and entropy method. From this correlation distances, respective influencing radii of each dataset and each methods is assessed. The result of correlation and entropy analysis has estimated correlation distance of 25.546km and influence radius of 7.206km, deducing a decrease of network density from $224.53km^2$ to $122.47km^2$ which satisfy the recommended minimum densities of $250km^2$ in mountainous regions(WMO, 1994) and an increase of basin coverage from 59.3% to 86.8%. As for the elevation analysis the relative evaluation ratio increased from 0.59(current) to 0.92(optimized) resulting an obvious improvement.

• ETRI Journal
• /
• v.33 no.5
• /
• pp.770-779
• /
• 2011

Security quad and cube (SQC) is a network attack analyzer that is capable of aggregating many different events into a single significant incident and visualizing these events in order to identify suspicious or illegitimate behavior. A network administrator recognizes network anomalies by analyzing the traffic data and alert messages generated in the security devices; however, it takes a lot of time to inspect and analyze them because the security devices generate an overwhelming amount of logs and security events. In this paper, we propose SQC, an efficient method for analyzing network security through visualization. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacks. In addition, by providing a detailed analysis of network attacks, this method can more precisely detect and distinguish them from normal events.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.1
• /
• pp.23-35
• /
• 2017

With the popularization of mobile devices, the number of social network service users is increasing, thereby the amount of data is also increasing accordingly. As Internet of Things environment is expanding to connect things and people, there is information much more than before. In such an environment, it becomes very important to recommend the necessary information to the user. In this paper, we propose a recommendation method that considers new users in IoT environment. In the proposed method, we recommend the information by applying the centrality-based social network analysis method to the recommendation method using the social relationships in the social IoT. We describe the seven-step recommendation method and apply them to the music circle scenario of the IoT environment. Through the music circle scenario, we show that we can recommend more suitable information to new users in the IoT environment than the existing recommendation method.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2986-2988
• /
• 2005

To improve the reliability of power-line communication systems, the measurement and analysis has been proceeded in many power-line channel environments. In spite of the wired channel, power line channel has many multi-paths that are changing with load-variation, line-interconnection, impedance mismatching and so on. We accordingly need an analysis method based on the multi-path channel impulse response. Recently, a method to describe the homogeneous Power-line network has been published[1]. In this paper the modified method that can describe both the homogeneous and non -homogeneous power-line network has presented.