• 한국수자원학회논문집
• /
• 제53권1호
• /
• pp.45-56
• /
• 2020

본 연구는 SWAT-CUP을 이용한 SWAT 모형 매개변수 보정을 수행할 때, 목적함수로 인해 발생할 수 있는 불확실성을 정량화하는 것을 목표로 수행되었다. 먼저 낙동강 권역의 내성천 유역을 대상으로 유출량 산정을 위한 SWAT 모형을 구축한 후, SWAT-CUP을 이용하여 8개 목적함수(R², bR², NS, MNS, KGE, PBIAS, RSR 및 SSQR)를 기준으로 자동 보정을 수행하였다. 최종 매개변수는 목적함수에 따라 서로 다른 범위를 나타내었으며, 모의 결과의 수문특성 또한 상이하게 도출되는 것을 확인하였다. 이것은 각각의 목적함수가 특정 수문특성에 대하여 민감하게 반응하여 서로 다른 모의 성능을 평가하기 때문이다. 즉, 특정 목적함수는 극치값의 잔차에 대해 민감하게 반응하여 첨두값을 잘 모의하는 반면, 저유량 또는 평균유량에 대한 모의 성능이 떨어질 수 있다. 따라서 본 연구에서는 최적 목적함수를 선정하기 위해 8개의 목적함수에 따라 산정된 모의값과 관측값 사이의 수문학적 유사성을 평가하였다. 단순히 유량의 크기 비교 뿐 아니라 유량의 발생 시기, 유역의 반응 및 증가·감소 경향성을 함께 고려하기 위해 수문곡선의 증수부 및 감수부 유지시간 비율을 수문특성으로 정의하여 SWAT 모형을 평가하였으며, 평가 결과를 점수로 정량화하여 나타냈다. 그 결과 최종적으로 SWAT 매개변수 보정을 위한 최적 목적함수는 총점이 높은 MNS (342.48) 및 SSQR (346.45)로 선정되었다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2010년도 학술발표회
• /
• pp.591-595
• /
• 2010

Conventional methods of model evaluation usually rely only on model performance based on a comparison of simulated variables to corresponding observations. However, this type of model evaluation has been criticized because of its insufficient consideration of the various uncertainty sources involved in modeling processes. This study aims to propose an extended model evaluation method using multiple assesment indices (MAIs) that consider not only the model performance but also the model structure and parameter uncertainties in rainfall-runoff modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250m, 500m, and 1km digital elevation models) were developed and assessed by three MAIs for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. In addition, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. Numerous parameter sets could lead to indistinguishable hydrographs. This result supports that while making a model complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty. The proposed model evaluation process can provide an effective guideline for identifying a reliable hydrologic model.

• Communications for Statistical Applications and Methods
• /
• 제24권6호
• /
• pp.651-662
• /
• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• 한국정밀공학회지
• /
• 제15권8호
• /
• pp.26-32
• /
• 1998

The grinding force generated during the grinding process causes an elastic deformation of the workpiece, grinding wheel, and machine system. Thus, the true depth of cut is always smaller than the apparent depth of cut. This is known as machining elasticity phenomenon. The machining elasticity parameter is defined as a ratio between the true depth of cut and the apparent depth of cut. It is an important factor to understand the material removal mechanism of the grinding process. To increase productivity, the value of this machining elasticity parameter must be large. Therefore, it is essential to know the characteristics of this parameter. The objective of this research is to study the effect of the major grinding conditions, such as table speed and depth of cut, on this parameter experimentally. Through this research, it is found that this parameter value is increasing when the table speed is decreasing or the depth of cut is increasing. Also, this parameter value depends on the grinding mode (up grinding, down grinding).

• Structural Engineering and Mechanics
• /
• 제22권4호
• /
• pp.401-418
• /
• 2006

Solutions to the problems of structural parameter estimation from modal response using leastsquares minimization of force or displacement residuals are generally sensitive to noise in the response measurements. The sensitivity of the parameter estimates is governed by the physical characteristics of the structure and certain features of the noisy measurements. It has been shown that the regularization method can be used to reduce effects of the measurement noise on the estimation error through adding a regularization function to the parameter estimation objective function. In this paper, we adopt the regularization function as the Euclidean norm of the difference between the values of the currently estimated parameters and the a priori parameter estimates. The effect of the regularization function on the outcome of parameter estimation is determined by a regularization factor. Based on a singular value decomposition of the sensitivity matrix of the structural response, it is shown that the optimal regularization factor is obtained by using the maximum singular value of the sensitivity matrix. This selection exhibits the condition where the effect of the a priori estimates on the solutions to the parameter estimation problem is minimal. The performance of the proposed algorithm is investigated in comparison with certain algorithms selected from the literature by using a numerical example.

• 한국자동차공학회논문집
• /
• 제15권4호
• /
• pp.161-170
• /
• 2007

As one of the major handling performances of the vehicle and tire, the steering feel is very important in the high speed where safety and refinement is a major concern for the drivers. This paper presents both subjective and objective techniques for the assessment of the steering feel including the on-center feel and steering response. For this, subjective evaluation method of the steering feel was studied at first and then objective parameters were selected by considering the process by which the steering feel is evaluated subjectively. From statistical analysis of subjective and objective data for the several vehicles and professional drivers, it was found that the subjective assessment of the steering feel could be successfully explained by means of the suggested objective parameters. Also, the main objective parameters related to the subjective assessment of the steering feel could be found.

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

• Journal of Mechanical Science and Technology
• /
• 제16권2호
• /
• pp.147-154
• /
• 2002

The objective of this paper is to develop a nondestructive method for estimating the fracture toughness (K$\_$IC/) of CrMoV steels used as the rotor material of steam turbines in power plants. To achieve this objective, a number of CrMoV steel samples were heat-treated, and the fracture appearance transition temperature (FATT) was determined as a function of aging time. Nonlinear ultrasonics was employed as the theoretical basis to explain the harmonic generation in a damaged material, and the nonlinearity parameter of the second harmonic wave was the experimental measure used to be correlated to the fracture toughness of the rotor steel. The nondestructive procedure for estimating the 7c consists of two steps. First, the correlations between the nonlinearity parameter and the FATT are sought. The FATT values are then used to estimate K$\_$IC/, using the K$\_$IC/ versus excess temperature (i.e., T-FATT) correlation that is available in the literature for CrMoV rotor steel.

• Ocean Systems Engineering
• /
• 제5권2호
• /
• pp.91-107
• /
• 2015

Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

• International Journal of Control, Automation, and Systems
• /
• 제1권3호
• /
• pp.289-300
• /
• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.