• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• 한국항공우주학회지
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• 제39권4호
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• pp.314-320
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• 2011

본 연구에서는 2단으로 구성된 달착륙선 충격 흡수 장치에 대한 최적화를 수행하였다. 충격 흡수 장치의 복잡한 충격거동을 모사하기 위해 1차원 구성방정식 모델을 제안하였으며, 이와 함께 상용해석 소프트웨어인 ABAQUS를 활용하여 최적화를 위한 2차 다항회귀 메타모델을 구성하였다. 구성된 메타모델을 순차적 근사 최적설계 기법에 적용하여 2단 충격 흡수 장치의 최적화를 수행하였으며, 이를 통해 허니컴 구조를 이용한 충격 흡수장치의 셀크기와 포일 두께를 변화시킴에 따라 달착륙선의 월면 착륙 시 충격하중을 크게 저감시킬 수 있음을 확인하였다.

• 동력기계공학회지
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• 제19권4호
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• pp.24-29
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• 2015

This paper designs a looper-tension controller for strip top-tail parts in hot strip finishing mills. A three-degree linear model of the looper-tension system is derived by a Taylor's linearization method, where the actuator's dynamics are ignored because of their fast responses. A feedforward shaping controller for the strip top part and a feedforward model reference controller for the strip tail part are respectively designed, they are combined with an ILQ(Inverse Linear Quadratic optimal control) feedback controller for the strip middle part. It is shown from by a computer simulation that the proposed controller is very effective to the strip top-tail parts including the middle part.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• 대한영양사협회학술지
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• 제15권4호
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• pp.356-363
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• 2009

The study aimed to determine the optimal mixing ratio of two different amounts of Letinus edodes powder and egg for preparation of pasta. The complete analysis was conducted using the Design Expert 7 program (State-Easy, Minneapolis, MN). Response surface methodology revealed 10 experimental points, including two replicates for L. edodes powder and egg. L. edodes pasta formulation was optimized using rheology. Lightness and redness displayed a lineal model pattern, whereas yellowness was represented by a quadratic model. The sensory evaluation parameters of texture, color, flavor, appearance and overall quality showed significant differences for color (p<0.01), flavor (p<0.05), texture (p<0.05) and overall quality (p<0.05). All sensory parameters showed significant values in a quadratic model. The optimum formulations processed by numerical and graphical optimization were determined as 15 g of L. edodes powder and 37.5 g of egg.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.337-346
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• 2019

This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.

• 소성∙가공
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• 제32권3호
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• pp.136-144
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• 2023

This study analyzed the mechanical behavior of lithium-ion battery pouch material and predicted its formability. A homogenization method was used to evaluate the physical properties of the pouch, and a new hardening model was developed. The yield function for the plastic model was optimized, and the anisotropic property was determined. Also, the forming limits were measured and predicted using the M-K forming limit diagram. Finally, a square cup drawing experiment confirmed the accuracy of the measured mechanical properties and the formability calculation.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.303-306
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• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.