• 대한전기학회논문지:전력기술부문A
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• 제51권8호
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• pp.398-402
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• 2002

The ELD computation has been based upon the so-called B-coefficient which uses a quadratic approximation of system loss as a function of generation output. Direct derivation of system loss sensitivity based on the Jacobian-based method was developed in early 1970s', which could eliminate the dependence upon the approximate loss formula. However, both the B-coefficient and the Jacobian-based method require a complicated Procedure for calculating the system loss sensitivity included in the constraints of the optimization problem. In this paper, an ELD formulation in which only the bus power equations are defined as the constraints has been introduced. Derivation of the partial derivatives of the system loss with respect to the generator output and calculation of the penalty factors for individual generators are not required anymore in proposed method. A comprehensive solution procedure including calculation of the Jacobians and Hessians of the formulation has been presented in detail. Proposed ELD formulation has been tested on a sample system and the simulation indicated a satisfactory result.

• 한국유체기계학회 논문집
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• 제16권1호
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• pp.40-46
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• 2013

Recently, the duct system with a cross flow fan was used to improve the ventilation in various industrial fields. For the efficient ventilation, it is necessary to design the duct system based on the flow characteristics around the cross flow fan. In the present study, the flow characteristics around a cross flow fan in the ventilation duct were predicted by using the moving mesh and sliding interface techniques for the rotation of blades. To design the duct system with the high performance of ventilation, the CFD simulations were repeated with the revised duct model based on the DOE. With the numerical results of flow rate through the ventilation duct with various geometric parameters, the optimized geometry of ventilation duct to maximize the flow rate was obtained by using the Kriging approximation method. From the performance curves of cross flow fan in the original and optimized models of ventilation duct, it was observed that the flow rate through the optimized model is about 16 percent larger than that through the original model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1314-1319
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• 2008

Fatigue reliability problems are nowadays actively considered in the design of mechanical components. Recently, Dimension Reduction Method using Kriging approximation (KDRM) was proposed by the authors to efficiently calculate statistical moments of the response function. This method, which is more tractable for its sensitivity-free nature and providing the response PDF in a few number of analyses, is adopted in this study for the reliability analysis. Before applying this method to the practical fatigue problems, accuracies are studied in terms of parameters of the KDRM through a number of numerical examples, from which best set of parameters are suggested. In the fatigue reliability problems, good number of experimental data are necessary to get the statistical distribution of the S-N parameters. The information, however, are not always available due to the limited expense and time. In this case, a family of curves with prediction interval, called P-S-N curve, is constructed from regression analysis. Using the KDRM, once a set of responses are available at the sample points at the mean, all the reliability analyses for each P-S-N curve can be efficiently studied without additional response evaluations. The method is applied to a spring design problem as an illustration of practical applications, in which reliability-based design optimization (RBDO) is conducted by employing stochastic response surface method which includes probabilistic constraints in itself. Resulting information is of great practical value and will be very helpful for making trade-off decision during the fatigue design.

• Interaction and multiscale mechanics
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• 제1권1호
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• pp.103-121
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• 2008

This paper presents an optimization-based method for computing a minimal bounding ellipsoid that contains the set of static responses of an uncertain braced frame. Based on a non-stochastic modeling of uncertainty, we assume that the parameters both of brace stiffnesses and external forces are uncertain but bounded. A brace member represents the sum of the stiffness of the actual brace and the contributions of some non-structural elements, and hence we assume that the axial stiffness of each brace is uncertain. By using the $\mathcal{S}$-lemma, we formulate a semidefinite programming (SDP) problem which provides an outer approximation of the minimal bounding ellipsoid. The minimum bounding ellipsoids are computed for a braced frame under several uncertain circumstances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• 한국항공우주학회지
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• 제34권9호
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• pp.11-19
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• 2006

초음속 축대칭 흡입구의 형상 설계를 위하여 원추 유동 해법과 충격파 근사 기법을 기반으로 하고 구배 기반의 최적화 기법을 이용한 설계 프로그램을 개발하였다. 압력 회복률과 항력을 고려한 두 가지 목적 함수에 대하여 충격파 위치와 카울의 형상, 흡입관 목 면적 등에 대한 제한 조건 등을 고려하여 여러 운용 조건에 대해 흡입구 설계를 수행하였다. 최적 설계를 위하여 압력 회복률과 항력을 동시에 고려한 목적 함수를 제안하고 압력 회복률만을 고려하여 설계된 흡입구 형상과 비교하였다. 설계된 결과는 전산 유체 역학을 이용한 비점성/점성 유동 해석으로부터 산출된 흡입구 성능 결과와 비교하여 검증하였으며 예측된 성능이 계산된 결과와 잘 일치하였다.

• Structural Engineering and Mechanics
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• 제64권4호
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• pp.413-426
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• 2017

Probabilistic methods are used in engineering where a computational model contains random variables. The proposed method under development: Direct Optimized Probabilistic Calculation (DOProC) is highly efficient in terms of computation time and solution accuracy and is mostly faster than in case of other standard probabilistic methods. The novelty of the DOProC lies in an optimized numerical integration that easily handles both correlated and statistically independent random variables and does not require any simulation or approximation technique. DOProC is demonstrated by a collection of deliberately selected simple examples (i) to illustrate the efficiency of individual optimization levels and (ii) to verify it against other highly regarded probabilistic methods (e.g., Monte Carlo). Efficiency and other benefits of the proposed method are grounded on a comparative case study carried out using both the DOProC and MC techniques. The algorithm has been implemented in mentioned software applications, and has been used effectively several times in solving probabilistic tasks and in probabilistic reliability assessment of structures. The article summarizes the principles of this method and demonstrates its basic possibilities on simple examples. The paper presents unpublished details of probabilistic computations based on this method, including a reliability assessment, which provides the user with the probability of failure affected by statistically dependent input random variables. The study also mentions the potential of the optimization procedures under development, including an analysis of their effectiveness on the example of the reliability assessment of a slender column.

• 대한기계학회논문집A
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• 제33권10호
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2005년도 추계학술대회 논문집
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• pp.289-295
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• 2005

LMTT(Linear Motor-based Transfer Technology)는 항만 자동화를 위한 수평 이송 시스템이며, 셔틀카(shuttle car)와 격자구조의 레일에 부착된 스테이터 모듈(stator module)로 구성된 PMLSM(Permanent Magnetic Linear Synchronous Motor)에 의해 구동된다. 본 논문에서는 LMTT시스템에서 컨테이너 운반을 담당하는 셔틀카(shuttle car)를 구성하는 부품인 이동체(mover)의 경량화를 위하여 직교배열표 및 크리깅 방법을 이용하여 최적설계를 수행한다. 설계변수로는 가로빔, 세로빔, 휠빔의 두께를 제한조건 함수로는 안전율이 고려된 응력을 넘지 않도록 설정하였다. 목적함수로는 중량을 설정하였다. 본 연구에서 제시된 방법으로 구한 최적해는 크리깅 내삽법(Kriging interpolation)으로 알려진 DACE(Design and Analysis of Computer Experiments) 모델을 엑셀(Excel)로 수식화하고 구했으며, GENESIS를 이용하여 민감도기반 최적설계로 구해진 최적해와 비교 및 검토하였다.