• 한국콘텐츠학회논문지
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• 제11권12호
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• pp.814-821
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• 2011

256 다중 검출기 전산화단층촬영 (multi detector computed tomography, MDCT)에서 두개부 전용 팬톰을 이용하여 분해능 파라메터와 재구성 필터의 영상 품질을 평가하고자 하였다. 사용한 장비는 256 MDCT 을 사용하였으며 philips system head phantom을 이용하여 Extended Brilliance Workspace에서 영상의 질 평가를 측정하였다. 장비에서 지원하고 있는 분해능 파라메터와 재구성 필터가 화질에 미치는 영향을 알아보기 위해서 검사조건을 $512{\times}512$ matrix, $128{\times}0.625$ mm beam collimation, 120 kVp, 250 mAs, 0.5 sec, 250 mm field of view (FOV), 절편 두께와 절편 간격은 5 mm, 1.0 pitch로 동일하게 적용하였다. 분해능 파라메터은 'Standard', 'High', 'Ultrahigh'로 구분하여 적용하였으며, 재구성 필터는 'A', 'B', 'C', 'D', 'UA', 'UB', 'UC' 등으로 바꿔가면서 영상을 재구성하여 노이즈, 균일도, 직선성, 변조전달함수 (modulation transfer function, MTF)의 50%와 10%를 측정하였다. 그 결과 'High' 분해능은 균일도, 직선성, MTF 50%와 10%에서 우수하였다. 'UA', 'UB' 재구성 필터는 균일도와 노이즈 평가에서 양호했으며 'C'재구성 필터는 직선성과 MTF 50%와 10%에서 양호하였다.

• 한국수자원학회논문집
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• 제50권3호
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• pp.191-200
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• 2017

본 연구에서는 부정류 계산모형의 안정적인 매개변수를 선정하기 위하여, 다수 지점의 관측치를 고려한 모형보정의 결과로부터 얻은 파레토 최적화와 최소최대 후회도 방법(minimax regret approach, MRA)을 결합하는 방법을 제안하였다. 여러 지점의 관측치를 고려한 모형의 보정은 다목적 최적화 문제로서, 통합접근법을 적용하여 최적해를 구하였다. 통합접근법은 여러 지점에 대한 가중치를 결합하여 하나의 목적함수를 얻고, 여러 번의 개별 최적화를 수행함으로써 다수의 파레토 최적해들을 구하는 방법이다. 이때 유량에 따른 조도계수의 가변성을 나타내는 두 개의 매개변수로 구성된 관계식을 이용하여 두 구간에 대한 매개변수들을 모형의 추정 대상 매개변수로서 최적화하였다. 이후 각기 다른 홍수사상에 대해 보정과 검증을 수행하였으며 각각에 대한 평가지표의 후회도를 정량화하였고 이를 결합한 결합후회도를 산정하였다. 이를 기준으로 파레토 최적해들의 순위를 결정하였다. 계산결과 추정된 모형의 가변조도계수와 그로부터 얻은 두 개 지점에서의 표준화된 RMSE들은 두 지점에 대한 가중치의 조합에 따라 선택되는 매개변수 값에 따라 달라짐을 알 수 있었다. 본 연구에서 제시한 방법은 수문 및 수리모형의 다수의 관측지점의 자료를 이용한 매개변수 산정문제에 있어서 안정적인 해를 도출할 수 있다.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.77-82
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• 2010

An analytical method for robust design of the multi-loop proportional-integral (PI) controller is proposed for various types of multi-delay processes. On the basis of the direct synthesis and generalized IMC-PID approach, the analytical tuning rules of the multi-loop PI controller are firstly derived for achieving the desired closed-loop response, and the structured singular value synthesis is then utilized for the tradeoffs between the robust stability and performance by adjusting only one design parameter (i.e., the closed-loop time constant). To verify the superiority of the proposed method, the simulation studies have been conducted on a wide variety of multivariable processes. The multi-loop PI controller designed by the proposed method shows a fast, well-balanced and robust response with the minimum integral absolute error (IAE) in compared with other renowned methods.

• 한국해안·해양공학회논문집
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• 제29권2호
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• pp.121-127
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• 2017

구조물에 작용하는 실제 파랑은 다방향 불규칙파로써 장대 구조물의 경우 파의 위상차로 인해 파력의 감소효과가 발생한다. 본 연구에서는 이러한 효과가 파괴확률에 기여하는 정도를 파악하고 기존의 모듈형 방파제에 비해 어느 정도의 안정성이 확보되는지 확인하고자 기존 방파제 및 파력감소계수를 이용한 장대케이슨 방파제의 활동 파괴모드에 대해 신뢰성해석을 수행하였다. 그 결과 기존의 모듈형 케이슨 방파제보다 장대케이슨 방파제의 신뢰도지수가 더 높게 나타났고, 설계변수 중 유의파고가 가장 높은 영향을 미치는 것으로 확인되었다. 추가로 파력감소계수의 산정에 사용되는 변수들의 평균값 변화에 따른 신뢰성해석을 수행하였으며, 해석 결과 각 변수 값과 파력감소계수의 관계에서 확인된 경향이 평균값 변화에 따른 신뢰도지수 결과의 경향에 나타나는 것을 확인할 수 있었다.

• 대한산업공학회지
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• 제38권3호
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권3호
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• 제8권4호
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• Journal of Electrical Engineering and Technology
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• 제6권6호
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.635-644
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• 2004

A method of identifying structural parameters such as stiffness and damping coefficients at interfacial points of vibro-acoustic systems is suggested using an optimization technique. To identify the parameters using a numerical optimization algorithm, cost functions are defined. The cost function should be zero at the correct parameter values. To minimize the cost functions using an optimization technique, a design sensitivity analysis procedure is developed in the framework of the multi-domain FRF-based substructuring method. As a numerical example, a ladder-like structure problem is introduced. With known parameter values and different initial guesses of the parameters, convergence characteristics to the exact value are compared f3r the three cost functions. Investigating the contours of the cost functions, we find the first cost function has the largest convergent region to the correct value. As another practical problem, stiffnesses of engine mounts and bushings in a passenger car are identified. The numerical examples show that the proposed method is efficient and accurate far realistic problems.

• 한국소음진동공학회논문집
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• 제14권6호
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• pp.536-545
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• 2004

A method of identifying structural parameters such as stiffness and damping coefficients at interfacial points of vibro-acoustic systems is suggested using an optimization technique. To identify the parameters using a numerical optimization algorithm, cost functions are defined. The cost function should be zero at the correct parameter values. To minimize the cost functions using an optimization technique, a design sensitivity analysis procedure is developed in the framework of the multi-domain FRF-based substructuring method. As a numerical example, a ladder-like structure problem is introduced. With known parameter values and different initial guesses of the parameters, convergence characteristics to the exact value are compared for the three cost functions. Investigating the contours of the cost functions, we find the first cost function has the largest convergent region to the correct value. As another practical problem, the stiffnesses of engine mounts and bushings in a passenger car are identified. The numerical examples show that the proposed method is efficient and accurate for realistic problems.