• 한국해양공학회지
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• 제16권4호
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• Structural Engineering and Mechanics
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• 제52권1호
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• pp.19-33
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• 2014

Structural robustness refers to the ability of a structure to avoid disproportionate consequences to the original cause. Currently attentions focus on the concepts of structural robustness, and discussions on methods of robustness based structural design are rare. Firstly, taking basis in robust $H_{\infty}$ control theory, structural robustness is assessed by $H_{\infty}$ norm of the system transfer function. Then using the SIMP material model, robustness based design of grid structures is formulated as a continuum topology optimization problem, where the relative density of each element and structural robustness are considered as the design variable and the optimization objective respectively. Generalized elitist genetic algorithm is used to solve the optimization problem. As examples, robustness configurations of plane stress model and the rectangular hyperbolic shell model were obtained by robustness based structural design. Finally, two models of single-layer grid structures were designed by conventional and robustness based method respectively. Different interference scenarios were simulated by static and impact experiments, and robustness of the models were analyzed and compared. The results show that the $H_{\infty}$ structural robustness index can indicate whether the structural response is proportional to the original cause. Robustness based structural design improves structural robustness effectively, and it can provide a conceptual design in the initial stage of structural design.

• 한국정밀공학회지
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• 제18권12호
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• pp.116-123
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• 2001

An automated injection molding design methodology has been developed to optimize multiple quality attributes, which are usually in conflict with each other, in injection molded parts. For the optimization, commercial CAE simulation tools and optimization techniques are integrated into the methodology. To decal with the multiple objective problem the relative closeness computed in TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) is used as a performance measurement index for optimization multiple part defects. To attain robustness against process variation, Taguchi's quadratic loss function is introduced in the TOPSIS. Also, the modified complex method is used as an optimization tool to optimize objective function. The verification of the developed design methodology was carried out on simulation software with an actual model. Applied to production this methodology will be useful to companies in reducing their product development time and enhancing their product quality.

• 한국전산구조공학회논문집
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• 제26권4호
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• pp.213-221
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• 2013

이 논문에서는 반무한 고체영역의 표면에서 측정한 변위응답의 시간이력으로부터 유한요소망 연속기법을 이용해 탄성파 속도의 공간적 분포를 추정하는 역해석 문제를 소개한다. 반무한 영역에서의 역해석을 위해서는 해석 대상이 되는 유한영역의 경계에서 파동의 반사가 일어나지 않도록 하는 것이 중요하다. 이를 위해 유한영역의 경계면에 perfectly-matchedlayers(PMLs)라는 수치적 파동흡수층을 도입하였고, PML을 경계로 하는 유한영역에서 역해석 문제를 정의하였다. 이 문제를 탄성파동방정식을 구속조건으로 하는 최적화 문제로 표현하였으며, 라그랑주 승수법에 기초한 비구속 최적화 기법에 의해 탄성파속도의 최적 분포를 결정하였다. 해의 정확도와 수렴성을 높이기 위해 유한요소망 연속기법을 도입하여 점진적으로 밀도가 증가하는 유한요소망에 대해 연속적으로 역해석을 수행하였다. 1차원 예제들을 통해 유한요소망 연속기법을 이용한 역해석으로부터 탄성파속도의 분포를 정확히 추정할 수 있음을 확인하였으며, 측정 응답에 노이즈가 존재하는 경우에도 제안한 역해석 기법은 목표 탄성파속도 분포에 근사한 결과를 도출하였다.

• 품질경영학회지
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• 제28권1호
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• pp.78-94
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• 2000

The ever increasing demands for enhanced competitiveness of engineered products require a "designing-in-quality" strategy that can effectively and efficiently incorporate concepts of uncertainty, quality, and robustness into design. Engineered design optimization approaches that are typically carried out with respect to a single objective become inadequate to address these multiple set of requirements. This paper presents a design metric for a multi-attribute robust design problem with designer′s preferences on the performance accuracy and the performance precision. The use of this design metric as the robust optimal design criterion in multi-stage experimentation and modeling technique is presented. The effectiveness of the overall design procedure and the performance of the proposed design metric are tested with the aid of IC design and the results are discussed.

• Transactions on Control, Automation and Systems Engineering
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• 제4권4호
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• pp.289-295
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• 2002

In this paper, a linear matrix inequality(LMI)-based robust control method, which combines model predictive control(MPC) with the feedback linearization(FL), is presented for constrained nonlinear systems with parameter uncertainty. The design procedures consist of the following 3 steps: Polytopic description of nonlinear system with a parameter uncertainty via FL, Mapping of actual input constraint by FL into constraint on new input of linearized system, Optimization of the constrained MPC problem based on LMI. To verify the performance and usefulness of the control method proposed in this paper, some simulations with application to a flexible single link manipulator are performed.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.135-147
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• 2023

Skipped median is more robust than median when outliers are not symmetrically distributed. In this work, we propose a novel algorithm to estimate the skipped median. The idea of skipped median and the new algorithm are extended to regression problem, which is called least clipped absolute deviation (LCAD). Since our proposed algorithm for nonconvex LCAD optimization makes use of convex least absolute deviation (LAD) procedure as a subroutine, regularizations developed for LAD can be directly applied, without modification, to LCAD as well. Numerical studies demonstrate that skipped median and LCAD are useful and outperform their counterparts, median and LAD, when outliers intervene asymmetrically. Some extensions of the idea for skipped median and LCAD are discussed.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.11-18
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• 2003

The parametric approaches to sliding mode design are newly proposed for the class of multivariable systems. Our approach is based on an explicit formula for representing all the slid-ing modes using the Lyapunov matrices of full order. By manipulating Lyapunov matrices, the sliding modes which satisfy the design criteria such as the quadratic performance optimization and robust stability to parametric uncertainty, etc., can be easily obtained. The proposed ap-proach enables us to adopt a variety of Lyapunov- (or Riccati-) based approaches to the sliding mode design. Applications to the quadratic performance optimization problem, uncertain systems, systems with uncertain state delay, and the pole-clustering problem are discussed.

• 대한수학회보
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• 제55권4호
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• pp.1303-1315
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• 2018

For principal component analysis (PCA) to efficiently analyze large scale matrices, it is crucial to find a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to infinity is needed for the equivalence of the two problems. However, in this case, exact penalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and efficiency of the proposed method.

• 경영과학
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• 제12권2호
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• pp.105-127
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• 1995

This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.