• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.229-237
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• 2008

In this research, a MDO(multi-disciplinary design optimization) framework, which integrates aerodynamic and structural analysis to design an aircraft wing, is constructed. Whole optimization process is automated by a parametric-modeling approach. A CFD mesh is generated automatically from parametric modeling of CATIA and Gridgen followed by automatic flow analysis using Fluent. Finite element mesh is generated automatically by parametric method of MSC.Patran PCL. Aerodynamic load is transferred to Finite element model by the volume spline method. RSM(Response Surface Method) is applied for optimization, which helps to achieve global optimum. As the design problem to test the current MDO framework, a wing weight minimization with constraints of lift-drag ratio and deflection of the wing is selected. Aspect ratio, taper ratio and sweepback angle are defined as design variables. The optimization result demonstrates the successful construction of the MDO framework.

• Analytical Science and Technology
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• v.6 no.3
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• pp.267-274
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• 1993

The iterative regression optimization strategy using two parameters is described and applied to the separation of amino acids and peptides by means of micellar liquid chromatography. The parameters examined are concentration of surfactant and 2-propanol. This approach results in a efficient optimization using a small number of initial experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.112-115
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• 1998

본 논문은 퍼지 추론 시스템 모델의 최적화를 제시한다. 비선형적이고 복잡한 실시스템의 특성을 해석하는 방법으로써 시스템의 정적 혹은 동적 특성을 묘사하기 위해 퍼지 모델이 사용된다. 그러나 퍼지 시스템의 동정은 경험적 방법에 의해 규칙을 추출하기 때문에, 보다 논리적이고 체계적인 방법에 의한 추출 방법의 고찰이 필요하다. 제안된 규칙베이스 퍼지모델은 GA 및 퍼지규칙의 이론을 이용한 시스템 구조와 파라미터 동정을 시향한다. 두형태의 퍼지모델 방법은 간략추론 및 선형추론에 의해 시행된다. 본 논문에서는 퍼지 추론 시스템의 전반부 파라미터 동정을 통해 퍼지 입력공간을 정의함으로써 비선형 시스템을 표현한다. 전반부 파라미터의 동정세는 유전자 알고리즘을 사용하고, 후번부는 표준가우스 소거법을 사용하여 동정한다. 최적화는 유전자 알고리즘에 기초한 자동-동조 방법이며, 학습 및 데이터의 성능결과의 상호 균형을 얻기 위한 하중값을 가진 성능지수가 제시된다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1815-1816
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• 2007

본 논문에서는 데이터의 특성을 이용한 정보 입자 기반 퍼지 뉴럴 네트워크의 연속적 최적화를 제안한다. 데이터들간의 거리를 중심으로 C-Means 클러스터링 알고리즘을 이용하여 멤버쉽 함수를 정의하고 각 중심의 후반부 중심값을 이용하여 후반부 학습에 적용한다. 구조/파라미터 동정에 있어서 실수 코딩 기반 유전자 알고리즘을 이용하여 입력변수의 수, 입력 변수의 선택, 멤버쉽함수의 수, 후반부 형태와 같은 시스템의 입력 구조와 전반부 멤버쉽함수의 정점 및 학습율과 모멘텀 계수와 같은 파라미터를 최적으로 동정한다. 또한, 구조 연산과 파라미터 연산의 연속적 동조 방법을 이용하여 퍼지 뉴럴 네트워크를 최적화한다. 제안된 퍼지 뉴럴 네트워크는 삼각형 멤버쉽 함수를 이용하며, 후반부 추론에는 간략, 선형, 변형된 2차식을 이용한다. 제안된 퍼지 뉴럴 네트워크는 표준 모델로서 널리 사용되는 수치적인 예를 통하여 평가한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.616-618
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• 2002

본 논문에서는 음성인식 성능을 높이기 위한 기본적 단계인 음성과 비음성 부분의 경계를 추출하는 음성 경계 추출 방법을 제안한다. 음성경계 추출을 위한 특징들로는 시간영역 분할 파라미터인 ZCR, MA를 사용하고 주파수 영역 분할 파라미터로 주파수 대역 파워 에너지 (Frequency band power energy), 포만트 계수 (Formant coefficient)를 사용하였고 각 파라미터들을 이용하여 음성 경계를 결정할 때 경험에 의해 임계치를 결정하는 단점을 보안하기 위해서 신경망을 이용한다. 신경망의 가중치와 임계치들은 지도 학습을 통해 최적화 되고, 학습을 통해 구성된 망을 음성과 비음성의 경계치 구분에 사용한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.829-835
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• 2009

Parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used the signal-to-noise ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. However, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and control factors (design factors), and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.813-821
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• 2015

In this paper, we analyzed the problems of a batch-type sterilizer and design a continuous sterilizer to control the temperature deviation. The temperature deviation is analyzed with respect to design parameters such as a nozzle diameter, hole diameter, and nozzle length. The significant temperature parameters are optimized using the response surface methodology. An experimental apparatus is developed using the optimized design parameters. Using a field test, we show that the target temperature is obtained in about 7.3 minutes and the temperature deviation is improved about $0.84^{\circ}C$. The optimized parameters from the test are equal to the analytical parameters.

• Korean Journal of Optics and Photonics
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• v.9 no.6
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• pp.417-422
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• 1998

In this paper we introduce a method to optimize panel parameters and drive signals in a matrix-adressed bistable twsited-nematic (BTN) liquid crystal display (LCD) panel. We measured the effect of data pulses on optical switching characteristics in a BTN LC cell to model the effect theoretically. We introduce a weighting function to model the effect of data pulses on the switching energy as a function of time. Once the weighting function is known, we can estimate the maximum number of lines for multiplexing operation at a given frame rate by calculating the minimum data pulse width. By characterizing a unit cell as we change panel parameters (for example, d/p ratio), we can optimize parameters for high-speed operation. We found that our theoretical predictions agree very well with experimental results.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.867-875
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• 2003

This paper concerns continuous density HMM topology optimization. There have been several researches for HMM topology optimization. BIC (Bayesian Information Criterion) is one of the well known optimization criteria, which assumes statistically well behaved homogeneous model parameters. HMMs, however, are composed of several different kind of parameters to accommodate complex topology, thus BIC's assumption does not hold true for HMMs. Even though BIC reduced the total number of parameters of HMMs, it could not improve the recognition rates. In this paper, we proposed two new model selection criteria, HBIC (HMM-oriented BIC) and BIC_Anti. The former is proposed to improve BIC by estimating model priors separately. The latter is to combine BIC and anti-likelihood to accelerate discrimination power of HMMs. We performed some comparative research on couple of model selection criteria for online handwriting data recognition. We got better recognition results with fewer number of parameters.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.215-222
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• 2001

Neural network based models of semiconductor manufacturing processes have been shown to offer advantages in both accuracy and generalization over traditional methods. However, model development is often complicated by the fact that back-propagation neural networks contain several adjustable parameters whose optimal values unknown during training. These include learning rate, momentum, training tolerance, and the number of hidden layer neurOnS. This paper presents an investigation of the use of genetic algorithms (GAs) to determine the optimal neural network parameters for the modeling of plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide films. To find an optimal parameter set for the neural network PECVD models, a performance index was defined and used in the GA objective function. This index was designed to account for network prediction error as well as training error, with a higher emphasis on reducing prediction error. The results of the genetic search were compared with the results of a similar search using the simplex algorithm.