• 한국환경과학회지
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• 제4권1호
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• pp.71-80
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• 1995

생화학적 산소요구량(BOD) 및 용존 산소(DO)을 이용하여 여러구간이 있는 강에 대한 이산 상태공간모델은 설정하였다. 상호작용 예측방법을 이용하여, 상태변수에 시간지연이 존재하는 대규모 시스템에 적용가능한 계층적 최적화 방법을 기술하였다. 정상상태 오차를 해석적으로 구하고, 상수 목표티 추적문제에 있어서 정상상태 오차가 발생하지 않을 필요충분조겆을 규명하였다. 수질오염 모델에 대한 컴퓨터 모사를 통하여 기술한 알고리듬의 타당성을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.35-43
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• 2001

In this paper, we propose a design method for GBSB (generalized brain-state-in-a-box) based associative memories. Based on the theoretical investigation about the properties of GBSB, we parameterize the solution space utilizing the limited number of parameters sufficient to represent the solution space and appropriate to be searched. Next we formulate the problem of finding a GBSB that can store the given pattern as stable states in the form of constrained optimization problems. Finally, we transform the constrained optimization problem into a SDP(semidefinite program), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• 한국정보통신학회논문지
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• 제17권4호
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• pp.797-804
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• 2013

본 논문은 Particle Swarm Optimization(PSO)을 이용하여 고속 2차원 디지털필터의 설계방법을 제안하였다. 먼저 2차원 상태공간 디지털필터의 설계문제를 PSO에 적용하기 위하여 최소화 문제로써 형식화 과정이 논의된다. 제안된 PSO 알고리즘을 이용한 설계방법은 필터설계에서 요구되는 안정성을 보증하는 과정이 검토되어 개선된다. 본 논문에서 제안된 방법의 타당성을 설계예시를 통해 고찰한 결과, 설계된 디지털필터는 동일한 설계사양으로 기존의 설계방법으로 설계된 디지털필터보다 근사 및 라운드오프 오차 면에서 우수한 결과를 얻을 수 있었다. 아울러 제안된 2의 멱수가 필터계수인 2차원 상태공간 디지털필터는 승산기가 필요하지 않아 기존의 필터보다 연산과정에서 계산용량을 약 1/4로 줄일 수 있다는 것을 보였다.

• International Journal of Precision Engineering and Manufacturing
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• 제7권4호
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• pp.57-62
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• 2006

The quality of precision spindle units (S/Us) running on rolling bearings depends strongly on their structural parameters, such as the configuration and geometry of the S/U elements and bearing preloads. When S/Us are designed, their parameters should be optimized to improve the performance characteristics. However, it is practically impossible to state perfectly a general criterion function for S/U quality. Therefore, we propose to use a multicriteria optimization based on the parameter space investigation (PSI) method We demonstrate the efficiency of the proposed method using the optimization results of high-speed S/Us.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2011년도 한국우주과학회보 제20권1호
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• pp.31.1-31.1
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• 2011

In this study, we investigated alignment state estimation performances of the three methods i.e. merit function regression (MFR), differential wavefront sampling (DWS) and Multiple Design Configuration Optimization (MDCO). The three target optical systems are 1) a two-mirror Cassegrain system for deep space Earth observation, 2) intermediate size three-mirror anastigmat for Earth ocean monitoring, and 3) extremely large segmented optical system for astronomical observation. We ran alignment state estimation simulation for several alignment perturbation cases including 1mm to 10mm in decenter and from 0.1 to 1 degree in tilt perturbation error for the two-mirror Cassegrain system. In general, we note that MDCO shows more competitive estimation performance than MFR and DWS. The computational concept, case definition and the simulation results are discussed with implications to future works.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1870-1873
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• 2004

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• Structural Monitoring and Maintenance
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• 제1권4호
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• 전력전자학회논문지
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• 제25권3호
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• pp.227-234
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• 2020

This paper presents the methods for the modeling and parameter optimization of the electric vehicle battery. The state variables of the battery are defined, and the test methods for battery parameters are presented. The state-space equation, which consists of four state variables, and the output equation, which is a combination of to-be-determined parameters, are shown. The parameter optimization method is the key point of this study. The least square of the modeling error can be used as an initial value of the multivariable function. It is equivalent to find the minimum value of the error function to obtain optimal parameters from multivariable function. The SIMULINK model is presented, and the 10-hour full operational range test results are shown to verify the performance of the model. The modeling error for 25 degrees is approximately 1% for full operational ranges. The comments to enhance modeling accuracy are shown in the conclusion.