• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1842-1848
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• 2009

This paper summarizes a data modeling for developing the baseline network analysis software of the Korean energy management system (EMS). The study is concentrated on the following aspects. First, the data for operating the each application software are extracted. Some of the EMS network application softwares are selected for basis model. Those are based on the logical functions of each software and are not considered the other softwares. Second, the common data are extracted for equipment model and topological structure of power system in Korea. We propose the application common model(ACM) that can be applied whole EMS network application softwares. The ACM model includes the hierarchy and non-hierarchy power system structure, and is connected each other using the direct and indirect link. Proposed database model is tested using the Korea Electric Power Corporation(KEPCO) system. The real time SCADA data are provided for the test. Through the test, we verified that the proposed database structure can be effectively used to accomplish the Korean EMS system.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.95-105
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• 2005

Diesel engines have low specific fuel consumption, but high particulate emissions, mainly soot. Diesel soot is suspected to have significant effects on the health of living beings and might also affect global warming. Hence stringent measures have been put in place in a number of countries and will be even stronger in the near future. Diesel engines require either advanced integrated exhaust after treatment systems or modified engine models to meet the statutory norms. Experimental analysis to study the emission characteristics is a time consuming affair. In such situations, the real picture of engine control can be obtained by the modeling of trend prediction. In this article, an effort has been made to predict emissions smoke and NO$_{x}$ using cylinder combustion derived parameters and diesel particulate filter data, with artificial neural network techniques in MATLAB environment. The model is based on three layer neural network with a back propagation learning algorithm. The training and test data of emissions were collected from experimental set up in the laboratory for different loads. The network is trained to predict the values of emission with training values. Regression analysis between test and predicted value from neural network shows least error. This approach helps in the reduction of the experimentation required to determine the smoke and NO$_{x}$ for the catalyst coated filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1947-1953
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• 2008

This paper studies a system design of testifying next generation convergence network. Next generation convergence network includes such elements as not only various gateway systems interworking with conventional PSTN(Public Switched Telephone Network) but also various protocols communicating between gateway systems and softswitches or gateway controllers. Discussed are an effective system solution to verify functionalities and performance of protocols professing. From such discussion, the study identifies functional blocks and operational flows required for establishing a test system, and then with a basis of these proposes a system architecture. Finally this paper presents system design results and its implemented functional details.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.213-223
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• 2006

In this study, artificial neural network is applied to the evaluation of bearing capacity of the PHC auger-drilled piles at sites of domestic decomposed granite soils. For the verification of applicability of error back propagation neural network, a total of 168 data of in-situ test results for PHC auger-drilled plies are used. The results show that the estimation of error back propagation neural network provide a good matching with pile test results by training and these results show the confidence of utilizing the neural networks for evaluation of the bearing capacity of piles.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.549-551
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• 2004

This paper used OPNET to simulate video streaming service a test IP network and MPLS network with the traffic shaping that have with CQ_ LLQ algorithm, LSP of fixed bandwidth, policy of limitation users and measures parameters such as delay, throughput, packet loss. To verify the performance of video streaming service in IP network and MPLS network, two scenario that have same topology and traffic source. One is the simulation for best-effort service in pure IP network. The other is the simulation for QoS-enabled service in MPLS Network. Based on simulation result, the MPLS network with CQ_ LLQ algorithm and fixed LSP show advantage of the video streaming service QoS, specially delay and packet loss

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.202-210
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• 1994

In this paper, the network management system is implemented based on the analysis of reguirements and network operation and management for Flexible Automation. Network management is necessary, which controls and supervises the network resources in the communication network. By means of both analytical methods and queueing model, the delay time distributions due to the increasement of transmission data are obtained and analyzed. The operations of this network management system are certified through the test environments with the network adaptor and softwares for each layer.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.87-90
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• 2006

Nowadays, we can enjoy access to high speed network and advanced services of convergence between broadcasting and communication anywhere and anytime through a ubiquitous computing. So, now digital convergence devices come out constantly. These devices are required faster network environment for high speed data processing than before. In this paper, we describe the design of InfiniBnad network adapter, which is included two FPGA chipsets. When this adapter is installed to Digital Convergence Platform (DCP) network performance will be improved. The adapter includes 12channel serial ports for external communication and internally, uses PCI-Express bus. We have finished the test of high speed serial based network adapter through composing complete InfiniBand network and applied fabric management software. So, we have verified that it can be applied on DCP environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.62-69
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• 2012

This thesis proposes a new social network simulator, which can be used for the social network analysis (SNA). It is composed of three modules; initialization, network evolution, and output generation. For the network evolution module, we suggest a modified JGN (MJGN) based on JGN, the network evolution model developed by Jin, Girvan, and Newman. Arena, one of the most popular simulation tools, was used to model the agent based social network simulator. Lastly, some test results were presented to show the value of the proposed simulator when one performs SNA at the longitudinal point of view.