• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.10
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• pp.1-10
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• 2000

A rigorous modeling of ultra-low energy implantation is becoming increasingly more important as devices shrink to deep submicron dimensions. In this paper, we have developed an efficient three-dimensional Monte Carlo ion implantation model based on a modified Binary Collision Approximation(BCA). To this purpose, the modified electronic stopping model and the multi-body collision model have been taken into account in this simulator. The dopant and damage profiles show very good agreement with SIMS(Secondary Ion Mass Spectroscopy) data and RBS(Rutherford Backscattering Spectroscopy) data, respectively. Moreover, the ion distribution replica method has been implemented into the model to get a computational efficiency in a 3D simulation, and we have calculated the 3D Monte Carlo simulation into the topographically complex structure.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.85-94
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• 2020

In determining the feasibility of planning and launching railway transportation projects, various decision-making processes are essentially required. LCC(Life Cycle Cost) value including total construction cost and operation cost is estimated in approximation Model with rough guideline. In this study, modeling and simulation-based analysis method is proposed to support the decision making process of railroad transportation and derivation of LCC. Firstly, cost analysis model was constructed by collecting various existing rail transportation business data to enable analyze based on numerical data, and the result were analyzed by DOE(Design Of Experiments) and RSM (Response Surface Method) simulation. Professional commercial software tools were used for effective model construction and simulation. In order to verify the research results, the actual railroad transportation projects were selected, and the results of the analysis were compared.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.925-933
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• 2006

Using the physics-based model for the vibration isolation system, the model uncertainties are described. With the model including parameter perturbations, the robust controller to meet the robust performance and stability is designed through $\mu$-synthesis by DK-iteration. The order of controller is reduced by virtue of Hankel norm approximation technique to allow the efficient implementation in the real-time experimental environment without any performance degradation. The performance of the reduced $\mu$-controller is accessed in comparison with the original one. The experiments validate the superiority of the proposed control scheme against the model uncertainties and its applicability with varying payload.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.12 s.117
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• pp.1262-1271
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• 2006

Using the physics-based model for the vibration isolation system, the model uncertainties are described. With the model including parameter perturbations, the robust controller to meet the robust performance and stability is designed through $\mu$-synthesis by DK-iteration. The order of controller is reduced by virtue of Hankel norm approximation technique to allow the efficient implementation in the real-time experimental environment without any performance degradation. The performance of the reduced $\mu$-controller is accessed in comparison with the original one. The experiments validate the superiority of the proposed control scheme against the model uncertainties and its applicability with varying payload.

• Structural Engineering and Mechanics
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• v.62 no.3
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• pp.303-310
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• 2017

The model updating problems, which are to find the optimal approximation to the discrete quadratic model obtained by the finite element method, are critically important to the vibration analysis. In this paper, the structured model updating problem is considered, where the coefficient matrices are required to be symmetric and positive semidefinite, represent the interconnectivity of elements in the physical configuration and minimize the dynamics equations, and furthermore, due to the physical feasibility, the physical parameters should be positive. To the best of our knowledge, the model updating problem involving all these constraints has not been proposed in the existed literature. In this paper, based on the semidefinite programming technique, we design a general-purpose numerical algorithm for solving the structured model updating problems with incomplete measured data and present some numerical results to demonstrate the effectiveness of our method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.103-109
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• 2004

This paper presents a fairing method for reverse engineering of a free-formed surface. Utilizing measured data points, reverse engineering is a useful method to construct a CAD model from physical model. Measured data points should be faired since raw data may have outliers. A fairing algorithm of polynomial regression model generates smooth curves of approximation in this paper. The faired curves are utilized to construct a free-formed surface. For a verifying example, an impeller blade is digitized with a CMM to collect raw data on the surfacce and a CAD model is constructed. This research produces impeller blades with 5-axis machining center through the CAD model and compares them with a physical model. As a result the produced surface modeled with the fairing method gives less error than that without the fairing.

• Korean Management Science Review
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• v.25 no.3
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• pp.1-12
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• 2008

The Swaption is one of the popular Interest rates derivatives. In spite of such a popularity, the swaption pricing formula is hard to derived within the theoretical consistency. Most of swaption pricing model are heavily depending on the simulation technique. We present a new class of swaption model based on the multi-factor HJM levy-mixture model. A key contribution of this paper is to provide a generalized swaption pricing formula encompassing many market stylize facts. We provide an approximated closed form solution of the swaption price using the Gram-Charlier expansion. Specifically, the solution form is similar to the market models, since our approximation is based on the Lognormal distribution. It can be directly compared with the traditional Black's formula when the size of third and fourth moments are not so large. The proposed extended levy model is also expected to be capable of producing the volatility smiles and skewness.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Journal of Korea Port Economic Association
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• v.22 no.1
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• pp.175-198
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• 2006

This paper presents a network capacity model that can be used as an analytical tool for strategic planning and resource allocation for multimodal transportation systems. In the context of freight transportation, the multimodal network capacity problem (MNCP) is formulated as a mathematical model of nonlinear bi-level optimization problem. Given network configuration and freight demand for multiple origin-destination pairs, the MNCP model is designed to determine the maximum flow that the network can accommodate. To solve the MNCP, a heuristic solution algorithm is developed on the basis of a linear approximation method. A hypothetical exercise shows that the MNCP model and solution algorithm can be successfully implemented and applied to not only estimate the capacity of multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. Transportation agencies and planners would benefit from the MNCP model in identifying investment priorities and thus developing sustainable transportation systems in a manner that considers all feasible modes as well as low-cost capacity improvements.