• International journal of advanced smart convergence
• /
• 제5권2호
• /
• pp.1-7
• /
• 2016

Recently, with the development of advanced thin film devices comes the need for constant high quality vacuum as the deposition pressure is more demanding. It is for this reason our research seeks to understand how the variable design factors are employed in such vacuum systems. In this study, the effects of design factor applications on the vacuum characteristics were simulated to obtain the optimum design modeling of variable models on an ultra high vacuum system. The commercial vacuum system simulator, $VacSim^{(multi)}$, was used in our investigation. The reliability of the employed simulator was verified by the simulation of the commercially available models of ultra high vacuum system. Simulated vacuum characteristics of the proposed modeling aligned with the observed experimental behavior of real systems. Simulated behaviors showed the optimum design models for the ideal conditions to achieve optimal pressure, pumping speed, and compression ratio in these systems.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제49권3호
• /
• pp.145-156
• /
• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• International Journal of Aeronautical and Space Sciences
• /
• 제17권1호
• /
• pp.29-36
• /
• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제6권1호
• /
• pp.33-38
• /
• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• 한국소음진동공학회논문집
• /
• 제18권5호
• /
• pp.572-581
• /
• 2008

The performance of a barrel stave flextensional transducer is determined by the properties of its constituent materials and the effects of many structural parameters. In this study, with the finite element method, the structure of a barrel stave flextensional transducer was optimized to achieve the widest bandwidth while satisfying the requirements on pressure and center frequency. The optimization was carried out with the SQP-PD method for multi-variable minimization. The optimized barrel stave flextensional transducer satisfied all the required specifications.

• 한국지능시스템학회논문지
• /
• 제10권4호
• /
• pp.343-350
• /
• 2000

본 논문에서는, 최적 시스템을 위해서 FNN과 PNN에 기반을 둔 Multi-FPNN(다중 퍼지 다항식 뉴럴네트워크) 모델을 제안한다. 여기서 FNN 구조는 각각의 분리된 입력변수에 의해 분할된 퍼지 입력공간을 사용해서 설게되고, 간략 퍼지추론 방법과 오류 역전파 알고리즘을 이용한다. FNN은 더 좋은 출력성능을 얻기 위해 PNN과 결합한다. GMDH 방법에 기초한 PNN 구조의 각 노드는 1차 및 2차 고계 다항식의 두 형태를 사용하고, 그 노드의 입력의 입력은 2, 3, 4의 세 종류의 다변수 입력을 사용한다. 그리고 다중 FPNN 모델의 구조와 파라미터를 동정하기 위햐 HCM 크러스터링방법과 유전자 알고리즘을 사용한다. 여기서, 시스템을 위해 데이터 전처리 기능을 수행하는 HCM 클러스터링 방법은 입출력 공간분할에 의해 다중 FPNN 구조를 결정하기 위해 사용된다. 모델의 근사화와 일반화 능력 사이에 충분한 군형을 ？기 위해 하중계수를 가진 합성 성능지수(목적함수)를 사용한다. 데이터 개수, 비선형의 정도(입.출력 데이터 분포)에 위존하는 이 합성 목적함수의 하중계수의 선택 및 조절을 통하여 최적의 다중 FPNN모델을 설계하는 것이 유용하고 효과적임을 보인다. 본 연구는 두 개의 대표적 수치예의 도움으로 설명되고, 그 모델의 근사화 및 일만화 능력에 관련된 합성 성능 지수가 평가되고, 도한 토의된다.

• 한국초전도저온공학회:학술대회논문집
• /
• 한국초전도저온공학회 2001년도 학술대회 논문집
• /
• pp.84-88
• /
• 2001

The optimum cross-sectional area Profile of gas-cooled high-temperature superconductor (HTS) current lead is analyzed to have minimum helium boil-off rate. The conventional constant area HTS lead has much higher helium consumption than the optimum HTS lead considered in this study. The optimum HTS lead has variable cross-sectional area to have constant safety factor. An analytical formula of optimum shape of lead and temperature profile are obtained. For multi-step HTS current leads, the optimum tape lengths and minimum heat dissipation rate are also formulated. The developed formulations are applied to the Bi-2223 material, and the differences between constant area, constant safety-factor, and multi-step current leads are discussed.

• 설비공학논문집
• /
• 제7권1호
• /
• pp.1-12
• /
• 1995

In order to seek various relationships among many design parameters employed in the design of the axial-flow fans the program which generates acoustic spectrum has been developed and its validity verified. Outputs of the program, with other outputs from a formerly developed performance prediction program, have been used to form a multi-objective function, for which an optimal design process was carried out. The present analysis shows that overall noise level and efficiency has contrasting trends, and the chord length turns out to be the most critical design variable. In the chosen design case of requirements $Q=2000m^2/min$, ${\Delta}P_s=67mmAq$, D=1.4m, the chord length of 0.2059m minimizes the overall noise level, while chord length of 0.1254m maximizes the efficiency. The resulting chord length in the balanced optimization is 0.1809m.

• 대한기계학회논문집
• /
• 제18권8호
• /
• pp.1985-1996
• /
• 1994

A new design method of sliding hyperplanes is proposed in the synthesis of a variable structure controller for robust tracking of general nonlinear multi-input-output(MIMO) uncertain systems of relative degree higher than two. Input/ output(I/O) linearzation is firstly undertaken by employing the concept of relative degree and minimum phase followed by the construction of sliding mode controllers. Sliding hyperplanes are then derived from the inherent properties of companion matrix and ideal sliding mode characterized in I/O linearized system. Subsequently, the gradient magnitudes of the sling hyperplanes are determined in an optimal manner by considering a quadratic performance index to be evaluated at two phases; a reaching phase and a sliding phase. The proposed design methodology is relatively straightforward and systematic compared with conventional strategies such as geometric approach or pole assignment technique. A nonlinear governor and exciter control problem for a power system is adopted herein in order to demonstrate the design efficiency and also favorable and robust control performances.

• 한국유체기계학회 논문집
• /
• 제7권6호
• /
• pp.55-61
• /
• 2004

A numerical procedure for optimizing the shape of three-dimensional sedimentation tank is presented to maximize its sedimentation efficiency. The response surface based optimization is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis for multi-phase flow. Standard $k-{\epsilon}$ model is used as a turbulence closure. Three design variables such as, tank height to center feed wall diameter ratio, blockage ratio of center feed wall and angle of distributor are chosen as design variables. Sedimentation efficiency is defined as an objective function. Full-factorial method is used to determine the training points as a means of design of experiment. Sensitivity of each design variable on the objective function has been evaluated. And, optimal values of the design variables have been obtained.