• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.31.2-31
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• 2001

A real-time control strategy is presented for plasma control Rather than in-situ plasma variables, this is based on realtime measurements of two electrical positions that correspond to two match motors. Using the rf match monitor system, the positions were collected. The process of impedance matching was identified with variations in process factors, including rf power, pressure, and O$_2$ flow rate. A state-space model was obtained basing on autoregressive moving average model. For this model, a linear quadratic regulator was designed and applied. Simulation results revealed that match positions could accurately be regulated to follow certain positions arbitrarily chosen.

• Journal of the Korean Statistical Society
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• 제16권2호
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• pp.71-79
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• 1987

The linear and quadratic log contrast model with mixtures on the strictly positive simplex, $$ x_{q-1} = {(x_1, \cdots, x_q):\sum x_, = 1 and \delta \leq \frac{x_i}{x_j} \leq \frac{1}{\delta} for all i,j},$$ are considered. Using the invariance arguments, symmetric D-optimal designs are investigated. The class of symmetric D-optimal designs for the linear log contrasts model is given. Any D-optimal design for the quadratic log contrast model is shown to metric D-optimal designs for q=3 and 4 cases are given.

• East Asian mathematical journal
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• 제5권1호
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• 재활복지공학회논문지
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• 제5권1호
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• pp.59-64
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• 2011

인공 중이 (IMEHD, implantable middle ear hearing device)용 플로팅 매스 트랜스듀서는 제작 과정상에서 발생하는 구조적 오차 및 제작 결합시의 문제점들로 인해 설계 시 의도와 다른 진동 특성을 가질 수 있게 되고, 이렇게 제작된 진동 트랜스듀서는 향후 실제 환자에게 이식되었을 때의 진동체 진동 성능을 예측하기 어려운 문제점이 있다. 본 연구에서는 제작된 플로팅 매스형 트랜스듀서에 대해 설계값과의 비교 평가 및 향후 컴퓨터 모델화된 청각 중이 집중 소자 모델에 적용이 가능하도록 기 제작된 진동체의 집중 소자 모델 파라미터들을 추정할 수 있는 방법을 제안하였으며, 제안된 방법을 LabVIEW 기반의 그래픽 유저 인터페이스 소프트웨어로 구현하였다. 제안된 방법은 실제 제작된 플로팅 매스형 진동체의 진동 변위 및 위상 데이터를 이용하여 해당 진동체의 집중 모델 파라미터들을 순차적 이차 프로그래밍 (SQP, sequential quadratic programming) 방법으로 추정하는 방식이다. 구현된 방법을 이용하여 측정 데이터에 가해지는 잡음의 양에 따른 플로팅 매스형 진동 트랜스듀서의 집중 모델 파라미터 추정 시의 발생 오차 변화를 컴퓨터 시뮬레이션을 통해 조사하여 본 방법의 파라미터 추정 정확도를 확인하였다. 또한, 실제로 제작한 인공중이용 진동 트랜스듀서에 대해 측정된 진동 변위 및 위상 데이터를 이용하여 진동체의 집중 모델 파라미터들을 추정하였으며, 기존 설계값들과 비교를 통해 제안된 방법의 유용성을 평가하였다.

• 응용통계연구
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• 제24권1호
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• pp.61-69
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• 2011

다양한 GARCH류 모형들의 변동성 함수를 살펴보면 흥미롭게도 거의 대부분 모형에서 수익률의 일차항( rst or der term)이나 수익률과 변동성의 교차항(interaction term)이 나타나지 않는다. 일차항과 교차항은 변동성의 비대칭성을 설명하는 역할을 할 수 있으며 $h_t$의 회귀분석식의 형태로 볼 때 변동성 함수의 일반적인 이차형식(quadratic form)을 구성한다고 할 수 있다. 본 논문에서는 변동성과 수익률들 사이의 교차항 및 일차항을 포함한 이차형식(quadratic form) 변동성 모형들을 소개하고, 국내 금융시계열 자료에 적용한 후 비교 분석하고자 한다.

• 한국경영과학회지
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• 제41권2호
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• pp.129-139
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• 2016

This paper presented a robust ship scheduling model using the quadratic programming problem. Given a set of available carriers under control and a set of cargoes to be transported from origin to destination, a robust ship scheduling that can minimize the mean-variance objective function with the required level of profit can be modeled. Computational experiments concerning relevant maritime transportation problems are performed on randomly generated configurations of tanker scheduling in bulk trade. In the first stage, the optimal transportation problem to achieve maximum revenue is solved through the traditional set-packing model that includes all feasible schedules for each carrier. In the second stage, the robust ship scheduling problem is formulated as mentioned in the quadratic programming. Single index model is used to efficiently calculate the variance-covariance matrix of objective function. Significant results are reported to validate that the proposed model can be utilized in the decision problem of ship scheduling after considering robustness and the required level of profit.

• 한국기계기술학회지
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• 제20권6호
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• Journal of Microbiology and Biotechnology
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• 제19권7호
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• pp.718-726
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• 2009

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37DC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias ($B_f$) and accuracy factors ($A_f$) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and $10^{\circ}C$. The $B_f$and $A_f$for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with $B_f$and $A_f$and % RE together.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권5호
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• pp.873-887
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• 2015

The springing response of a large blunt ship was considered to be influenced by a second order interaction between the incoming irregular wave and the blunt geometry of the forebody of the ship. Little efforts have been made to simulate this complicated fluid-structure interaction phenomenon under irregular waves considering the second order effect; hence, the above mentioned premise still remains unproven. In this paper, efforts were made to quantify the second order effect between the wave and vibrating flexible ship structure by analyzing the experimental data obtained through the model basin test of the scaled-segmented model of a large blunt ship. To achieve this goal, the measured vertical bending moment and the wave elevation time history were analyzed using a higher order spectral analysis technique, where the quadratic interaction between the excitation and response was captured by the cross bispectrum of two randomly oscillating variables. The nonlinear response of the vibrating hull was expressed in terms of a quadratic Volterra series assuming that the wave excitation is Gaussian. The Volterra series was then orthogonalized using Barrett's procedure to remove the interference between the kernels of different orders. Both the linear and quadratic transfer functions of the given system were then derived based on a Fourier transform of the orthogonalized Volterra series. Finally, the response was decomposed into a linear and quadratic part to determine the contribution of the second order effect using the obtained linear and quadratic transfer functions of the system, combined with the given wave spectrum used in the experiment. The contribution of the second order effect on the springing response of the analyzed ship was almost comparable to the linear one in terms of its peak power near the resonance frequency.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.107-113
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• 2011

We present methods for studying the log-density ratio, which allow us to select which predictors are needed, and how they should be included in the logistic regression model. Under multivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of many predictors. The linear, quadratic and crossproduct terms are required in general. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms.