• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.221-226
• /
• 1999

A data assimilation system for a 1-dimensional mixed layer model has been constructed using the adjoint method. The classical adjoint method does not work well for the mixed layer variabilities due to the occurrence of spikes in the gradient of the cost function. To solve this problem, the two techniques of scaling the cost function and optimization in the frequency space are used. As a result, the heat flux can be reliably estimated with an accuracy of 8Wm$^{-2}$ rms error in the identical twin experiments. We then applied this system to the tropical Pacific TOGA-TAO buoy data. The air-sea heat flux as well as the mixed layer variability were estimated in close approximation to the buoy data, particularly on time scales longer than the seasonal one.

• Korean Management Science Review
• /
• v.15 no.2
• /
• pp.125-142
• /
• 1998

The aggregation on linear large-scale dynamic systems is examined in this paper and a "two-step" approach is proposed. In this procedure, the aggregated system consists of two subsystems. The first subsystem represents aggregation through the retainment of dominant eigenvalues of the original system, leading to a first approximation of the desired output of the original system. The purpose of augmenting it with a second subsystem is to provide an estimation of the error on the first approximation, thus permitting a second correction to the output approximation and resulting in an output approximation of greater accuracy. Optimization techniques are discussed for the determination of unknown parameters in the aggregated system. These techniques use minimization principles of certain suitable performance indices and are developed for both single input-single output and multiple input-multiple output system. Numerical examples illustrating these procedures are given and the results are compared with those obtained using existing methods. Finally, a pharmacokinetics problem is studied from the aggregation point of view.

• Journal of the Speleological Society of Korea
• /
• no.82
• /
• pp.5-7
• /
• 2007

In this paper, p-n junction formation using screen-printed metalization and co-firing is used to fabricate high-efficiency solar cells on single- crystalline silicon substrates. In order to form high-quality contacts, co-firing of a screen-printed Ag grid on the front and Al on the back surface field is implemented. These contacts require low contact resistance, high conductivity, and good adhesion to achieve high efficiency. Before co-firing, a statistically designed experiment is conducted. After the experiment, a neural network (NN) trained by the error back-propagation algorithm is employed to model the crucial relationships between several input factors and solar cell efficiency. The trained NN model is also used to optimize the beltline furnace process through genetic algorithms.

• Journal of Korean Institute of Industrial Engineers
• /
• v.34 no.1
• /
• pp.23-31
• /
• 2008

This paper presents accelerated degradation test plans considering adoption of tightened critical values. Under arandom coefficient degradation rate and log-linear acceleration models, the asymptotic variance of an estimatorfor a lifetime quantile at the use condition as the optimization criterion is derived where the degradation ratefollows a lognormal and Reciprocal Weibull distributions, respectively and then the low stress level andproportions ofunits allocated to each stress level are determined. We also show that the developed test plans canbe applied to the multiplicative model with measurement error.

• MALSORI
• /
• no.66
• /
• pp.73-86
• /
• 2008

Beamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamformning, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Bark's scale spectral distortion (BSD).

• Journal of Power System Engineering
• /
• v.4 no.3
• /
• pp.72-80
• /
• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.9 no.6
• /
• pp.16-20
• /
• 2010

This study proposes a structural design method for the upper control arm installed at the rear side of a SUV. The weight of control arm can be reduced by applying the design. In this research, the former includes optimization technology, and the latter the technologies for selecting aluminum as a steel-substitute material. Strength assessment is the most important design criterion in the structural design of a control arm. At the proto design stage of a new control arm, FE (finite element) analysis is often utilized to predict its strength. In this study, the kriging interpolation method is adopted to obtain the minimum weight satisfying the strength constraint and durability criteria. The optimum results determined from the in-house program are compared with those of ANSYS WORKBENCH. The durability assessment is obtained by a index of fatigue durability and trial & error method, MSC. Fatigue program.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.10 no.3
• /
• pp.68-74
• /
• 2001

In the drawing of large size automotive panels, elastic deflection of die components is induced by the contact force between them. The deflection is nonuniform and locally distributed, and results in nonuniform material flow. In order to arrange such a nonuniform die gap, a correcting operation, so called die spotting, is inevitable, which requires trial and error works and consuming time. A prediction of the bending deflection prior to a try-out must be useful to reduce the die spotting time. In this study, drawing process of a front fender is simulated first. and the deflection of the blank holder is calculated from the contact force imposing on th blank holder. The balance block heights ensuring a uniform deflection are optimized by the analysis and design of experiments.

• International Journal of Aeronautical and Space Sciences
• /
• v.15 no.1
• /
• pp.74-81
• /
• 2014

This article documents the design of a novel two-loop acceleration autopilot based on $\mathcal{L}_1$ adaptive output feedback control for tail-controlled missiles. The inner loop is an adaptive angle-of-attack tracking loop and the outer loop is the traditional PI controller for error compensation. A systematic low-pass filter design procedure is provided for minimum phase system and is applied to the inner loop design while the parameters of the outer loop are obtained from the multi-objective optimization problem. The effectiveness of the proposed autopilot is verified through numerical simulations under various conditions.

• Journal of Mechanical Science and Technology
• /
• v.14 no.11
• /
• pp.1206-1215
• /
• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.