• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.107-114
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• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.82.1-82.1
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• 2019

Recent analyses of spectro-images of Saturn observed by Visual and Infrared Mapping Spectrometer (VIMS)/Cassini revealed altitudinal distributions of the spectral structure of haze in Saturn's south-polar regions (Kim et al., 2018) and at $55^{\circ}N$ latitude (Kim et al., 2012). However, other regions of Saturn still have not been investigated. We derived series of high-spatial resolution VIMS images of Saturn's limb at various latitudes. Using our developed code, the altitudinal intensity profiles of $3.3-{\mu}m$ emission and H3+ through different latitudes were plotted. Then we obtained the averaged vertical spectra of $3.3-{\mu}m$ emission which is all blended with fluorescent methane and hydrocarbon haze. The vertically-resolved spectra were measured from the limb of Saturn in 50km intervals to see altitudinal variance. We will present a comparison of spectral structures of $3.3-{\mu}m$ emission with different latitudes. Further investigation using radiative transfer to extract adjacent fluorescent CH4, C2H6, and H3+ is needed to derive spectral structure of pure haze. We look forward to a better understanding of aging process in a global view.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4389-4411
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• 2018

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.4
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• pp.575-581
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• 1998

In order to accommodate current accelerated growth in customers and traffic. Internet has faced the demand to scale its network dimension both in size and bandwidth, and new service provisioning. One way to solve this problem is to forward If packets based on ATM switching technology. This paper briefly explained technical tasks to apply this If switching technique in ATM networks for building Internet backbone, and presented the directions to approach these tasks. Those tasks are scalability, ATM VC setup and mapping between VC and IP packet flow, traffic management and traffic engineering, multicast, and finally ATM switch architecture to provide multiservice.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3C
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• pp.170-174
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• 2011

We introduce the network coding which cooperative communication for two-way relay channel. We propose a new network coding scheme using orthogonality for cooperative communication system. The proposed network coding scheme via orthogonal mapping shows better BER performance because proposed scheme weakens error propagation which is disadvantage of DF scheme. And proposed scheme maintains same throughput compared to conventional scheme.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.33-60
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• 2009

This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.5 no.2
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• pp.12-23
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• 1987

This paper deals with the analysis and adjustment computation for the Korean 1st order levelling data obtained by the National Geography Institute from 1974 to 1986. An evaluation of the accuracy of level lines using forward/back discrepancies and adjustment residuals has been carried out. A general assessment of the network in the light of current standards with other countries is discussed. The results of this study show that systematic errors are relatively high but the accuracy of the primary levelling is good enough within the limitation of precision levelling according to International Association of Geodesy(IAG) Resolution, and therefore it is concluded that this levelling will satisfy the present mapping and engineering survey.

• International Journal of Fluid Machinery and Systems
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• v.3 no.4
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• pp.369-378
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• 2010

The creation of the hydraulic turbine flow factor map will undoubtedly benefit its design by decreasing both the design cycle time and product cost. In this paper, the geometry and flow variables, which effectively affect the flow factor, are proposed, analyzed and determined. These flow variables are further used to create the operating condition maps by using different model approaches categorized into Response Surface Method (RSM) and Artificial Neural Network (ANN). The accuracies of models created by different approaches are compared and the performances of model approaches are analyzed. The influences of chosen variables and the combination of Principle Component Analysis (PCA) and model approaches are also studied. The comparison results between predicted and actual flow factors suggest that two-hidden-layer Feed-forward Neural Network (FFNN), and one.hidden-layer FFNN with PCA has the best performance on forming this mapping, and are accurate sufficiently for hydraulic turbine design.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.