• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1154-1158
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• 1995

Electrical Discharge Machining, with its ability to machine hard metals and tough shapes has become a very desirable process. In the past few years, Electrical Discharge Machining (EDM) has been solidly established in tool-room and large-scale production. However, in spite of its indispensability in many areas of metal removal applications, the theoretical basis of EDM process is yet to be established. More importantly, the information regarding essential technology parameters such as machining rate and resulting surface roughness integrity, has not been raised to the level of a general technical science. The paper presents a method, which can be determining approprate machining parameters for the given parameters such as electrode wear and surface roughness based on machining guideline utilizing neural networks.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.80-84
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• 2000

Network parameters in power systems are indispensable for all of power system engineering studies, including the power flow calculation and the state estimation. The network parameters required for the studios, in general, are estimated by using several estimation techniques, since it Is very difficult to measure. To improve the estimation accuracy of the network parameters, this paper adopt the synchronized phasor measurements which are acquired from the Phasor Measurement Unit with built-in GPS receiver. In this paper, the parameter estimation problem is formulated with over-determined nonlinear measurement equations and solved with Newton-Raphson method and pseudo-inverse. The effectiveness of the proposed parameter estimation with the synchronized phasor measurements is verified through some case studies with IEEE sample system. The results are very promising.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.176-179
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• 1998

In general, machining time by electrical discharge machining (EDM) process is much longer than that of cutting process, so rough-cut has done for the purpose of reducing machining time prior to EDM Nowadays EDM speed is improving due to the advance of EDM capacity. Therefore a new method, machining a raw material directly by EDM without rough-cut, is used widely. EDM area is varies according to the EDM position in three-dimensional EDM process, so EDM parameters should be determined adaptively based on the EDM area to increase productivity. However it is difficult to calculate EDM area corresponding to the EDM position the EDM workers who have experience in shop floor determine machining parameters by experience. This paper proposes a method for determining EDM parameters based on EDM area corresponding to EDM position.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.27-28
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• 2008

For stable and sustainable energy supply, distributed generator (DG) has become an essential and indispensable element from environmental and energy security perspectives. However, installation of DG in distribution systems may cause negative affects on feeders because power outputs of DG could be changed irregularly. One of major negative affects is variation in voltage profile. In general, voltage regulation devices such as under load tap changer (ULTC) at distribution substation and step voltage regulator (SVR) along feeder in distribution system are used to maintain customers' receiving voltage within a predetermined range. These regulators are controlled by line drop compensation (LDC) method which calls for two parameters; the equivalent impedance and the load center voltage. Therefore, consideration of DG outputs in the LDC parameter design procedure may give large impact on the installable DG capacity. This paper proposes a method that estimates maximum Installable DG capacity considering LDC parameters of ULTC and SVR. The proposed algorithm is tested with model network.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.440-440
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• 2000

In general, camera calibration is consisted of Indoor and Outdoor system. In case of Indoor system, it was optimized experimental condition. However, Outdoor system is different camera parameters for each image that is compared to equaled position. That is, it imply that camera parameters are varied by an environment with light or impulse noise, etc. So we make use of Image sequence because that they provide the more information for each image. In addition to, we use Corresponding line because it has less error than Corresponding point. Corresponding line has also the more information. In this paper, we suggest on-line camera calibration method using the time-varying Image sequence and Corresponding line. So we calculate camera parameters with intrinsic and extrinsic parameters in On-line system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.184-192
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• 1994

The parameters such as Richardson numbers or stability parameters are widely used to account for the extra straining effects due to three-dimensionality, curvature, rotation, swirl and others arising in paractical complex flows. Existing expressions for the extra strain in turbulence models such as $k-{\epsilon}$ models, however, do not satisfy the tensor invariant condition representing the coordinate indifference. In the present paper, considering the characteristics of both the mean strain rate and the mean vorticity, a new parameter to deal with the extra straining effects is proposed. The new parameter has a simple form and satisfies the tensor invariant condition. A semi-quantitative analysis between the present and previous parameters for several typical complex flows suggests that the newly proposed parameter is more general and adequate in representing the extra straining effects than the previous ad-hoc parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.3
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• pp.180-193
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• 2023

Approximating the implied volatilities and estimating the model parameters are important topics in quantitative finance. This study proposes an approximation formula for short-maturity near-the-money implied volatilities in stochastic volatility models. A general second-order nonlinear PDE for implied volatility is derived in terms of time-to-maturity and log-moneyness from the Feyman-Kac formula. Using regularity conditions and the Taylor expansion, an approximation formula for implied volatility is obtained for short-maturity nearthe-money call options in two stochastic volatility models: Heston model and SABR model. In addition, we proposed a novel numerical method to estimate model parameters. This method reduces the number of model parameters that should be estimated. Generating sample data on log-moneyness, time-to-maturity, and implied volatility, we estimate the model parameters fitting the sample data in the above two models. Our method provides parameter estimates that are close to true values.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.135-138
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• 2005

In this paper, we consider the problem of testing for parameter change in time series models based on a cusum of squares. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case was not discussed in literatures. Therefore, here we develop the cusum of squares type test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model. Simulation results are reported for illustration.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.43-48
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• 2005

We consider the problem of testing for parameter changes in time series models based on a cusum test. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case has not been discussed in the literature. Therefore, here we develop a cusum test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model and that of the autocovariances of a linear process. We also consider the variance change test for unstable models with unit roots and GARCH models.