• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.821-827
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• 2001

As one of the advanced design features of the Korea next generation reactor, direct vessel injection (DVI) system is being considered instead of conventional cold leg injection (CLl) system. It is known that the DVI system greatly enhances the reliability of the emergency core cooling (ECC) system. However, there is still a dispute on its performance in terms of water delivery to the reactor core during the reflood period of a large-break loss-of-coolant accident (LOCA). Thus, experimental validation is under progress. In this paper, a new scaling method, using time and velocity reduced linear scaling law, is suggested for the design of a scaled-down experimental facility to investigate the direct ECC bypass phenomena in PWR downcomer.

• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.276-284
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• 1999

In this paper, we propose a new technique which allows to store the pre-scaling constants directly in a table thus eliminating the cycle for computing pre-scaling constants. Especially we analyzed the range of pre-scalingconstants and rearranged them in a carry-save form using two look-up tables so that the size of the tables can be reduced significantly. The resulting scheme is compared with the previously developed method and shown to be effective with respect to area and time to implement the high-radix divider.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2562-2567
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• 2005

This paper suggests a new method discretization of nonlinear system using Taylor series expansion and zero-order hold assumption. This method is applied into the sampled-data representation of a nonlinear system with input time delay. Additionally, the delayed input is time varying and its amplitude is bounded. The maximum time-delayed input is assumed to be two sampling periods. Them mathematical expressions of the discretization method are presented and the ability of the algorithm is tested for some of the examples. And 'hybrid' discretization scheme that result from a combination of the ‘scaling and squaring' technique with the Taylor method are also proposed, especially under condition of very low sampling rates. The computer simulation proves the proposed algorithm discretized the nonlinear system with the variable time-delayed input accurately.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.33-40
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• 2007

In this paper, low-rate TCP attack as one of shrew attacks is considered and the scaling based dynamic time warping (S-DTW) algorithm is introduced. The low-rate TCP attack can not be detected by the detection method for the previous flooding DoS/DDoS (Denial of Service/Distirbuted Denial of Service) attacks due to its low average traffic rate. It, however, is a periodic short burst that exploits the homogeneity of the minimum retransmission timeout (RTO) of TCP flows and then some pattern matching mechanisms have been proposed to detect it among legitimate input flows. A DTW mechanism as one of detection approaches has proposed to detect attack input stream consisting of many legitimate or attack flows, and shown a depending method as well. This approach, however, has a problem that legitimate input stream may be caught as an attack one. In addition, it is difficult to decide a threshold for separation between the legitimate and the malicious. Thus, the causes of this problem are analyzed through simulation and the scaling by maximum auto-correlation value is executed before computing the DTW. We also discuss the results on applying various scaling approaches and using standard deviation of input streams monitored.

• Journal of electromagnetic engineering and science
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• v.13 no.3
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• pp.158-164
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• 2013

This paper expands a previously proposed optimized higher order (2, 4) finite-difference time-domain scheme (H(2, 4) scheme) for use with lossy material. A low dispersion error is obtained by introducing a weighting factor and two scaling factors. The weighting factor creates isotropic dispersion, and the two scaling factors dramatically reduce the numerical dispersion error at an operating frequency. In addition, the results confirm that the proposed scheme performs better than the H(2, 4) scheme for wideband analysis. Lastly, the validity of the proposed scheme is verified by calculating a scattering problem of a lossy circular dielectric cylinder.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.482-491
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• 2003

In this paper are presented kinematic and dynamic modeling and path-tracking of four-wheeled mobile robots with 2 d.o.f haying the limited drive-torques. Controllability of wheeled-mobile robots is revealed by the kinematic model. Instantaneously coincident coordinate system, force/torque propagation and Newton's equilibrium law are used to drive the dynamic model. When drive-torques generated by inverse dynamics exceed the limitation, we make wheeled-mobile robots follow the reference path by modifying the planned reference trajectory with time-scaling. The controller is introduced to compensate for error owing to modeling uncertainty and measurement noise. And simulation results prove that method proposed by this paper is efficient.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.7-14
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• 2009

In this paper we consider the problem of global finite-time stabilization for a class of uncertain nonlinear systems which include uncertainties. The uncertainties are time-varying disturbances or parameters belong to a known compact set. The proposed design method is based on backstepping and dynamic exponent scaling using an augmented dynamics, from which, a dynamic smooth feedback controller is derived. The finite-time stability of the closed-loop system and boundedness of the controller are preyed by the finite-time Lyapunov stability theory and a new notion 'degree indicator'.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.