• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2212-2215
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• 2002

This dissertation suggests and implements a middle level network which is called PICNET-NP (Plant Implementation and Control Network for Nuclear Power Plant). PICNET-NP is based partly on IEEE 802.4 token-passing bus access method and partly on IEEE 802.3 physical layer. For this purpose a new interface a physical layer service translator, is introduced. A control network using this method is implemented and applied to a distributed real-time system. To verify the performance of proposed protocol experimental were carried out, and the following results are obtained. 1) proper initialization of the protocol. 2) normal receiving and transmission of data. 3) proper switching of transmission media in case of a fault condition on the one of transmission media. The proposed protocol exhibits the excellent performance in the experimental system. From the test results in the experimental system, the proposed protocol, PICNET-NP, can be used for the upgrading of a nuclear power plant and the distributed control system in the next generation of nuclear power plant.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.361-368
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• 2004

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within an Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order learning performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• The KIPS Transactions:PartC
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• v.14C no.6
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• pp.525-536
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• 2007

Network Control Platform(NCP) and Qualify of Service Switch(QSS) are being developed to realize centralized control and management technology, which is essential for guaranteeing traffic engineering and service quality in a next generation network. This paper adopts a parallel mechanism, and a thread and object pool to achieve high-speed connection management in the existing SNMP interface between NCP and QSS. We built up a connection management test-bed in laboratory environment to validate the functionality of high-speed connection management. We also measured and analyzed a performance of connection setup delay and a completion ratio using the test-bed. We ascertain that the parallel mechanism and the object pool are the most important performance parameters to achieve high-speed connection management in the SNMP interface between NCP and QSS.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.64-72
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• 2004

Mobility changes the computing environment of computer users rapidly with the broad use of the Internet. In order to support mobility in the Internet infrastructure, IETF standardized Mobile IP With the use of Mobile IP, the Internet users can use the Internet freely without any change in the IP address configuration when they move from their home networks to foreign networks. In this paper, we implement a test bed for evaluation the performance of Mobile IP to use it in the campus network. By evaluating the effect of the use of Mobile IP on the network performance, we expect that the possible side effect of its direct use in the campus network will be reduced.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.10
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• pp.40-46
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• 2009

Recently CAN (Controller Area Network) is widely used as an in-vehicle networking protocol for intelligent vehicle. The identifier field (ID) of CAN is used not only to differentiate the messages but also to give different priorities to access the bus. This paper presents a dynamic 10 allocation algorithm in order to enhance the real-time quality-of-service (QoS) performance. When the network traffic is increased, this algorithm can allocate a network resource to lower priority message without degradation of the real-time QoS performance of higher priority message. In order to demonstrate the algorithm's feasibility, message transmission delays have been measured with and without the algorithm on an experimental network test bed.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.2096-2100
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• 2008

This research project management, building systems to test the performance requirements of a user to accept the plan and schedule management, personnel management, and test results, and the status of testing and reporting procedures to handle the process of implementation of Java-based X - Under the Internet environment, the default network, remote process research and development program as a test automation, structure and each module of the software analysis, design, analysis and clean-up and structure of the modules, each module and the GUI structure, performance, testing integration DB The present system is to study the system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.130-132
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• 2006

해당 논문에서는 많은 수의 서버로 구성된 환경에서의 자동화된 성능카운터 설정 및 수집 방안에 대해서 제시하고 대규모 시스템일 경우 많은 시간이 소요되고 번거로운 작업에 대한 자동화 및 카운터 설정, 제어를 위한 스크립트 제시, 리포트 자동 생성 방안을 제시하여 효율적인 시험 및 분석방안에 대해서 설명한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.