• 한국항공우주학회지
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• 제37권2호
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• pp.131-138
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• 2009

해석용 모델의 검증이란 완성된 모델이 실제 제품의 특성을 반영하고 있는지에 대한 확인절차이다. 일반적으로 해석모델작성 시 형상의 단순화 및 비선형특성의 반영에 대한 한계 등으로 공학적 가정을 이용하므로 실제 구조와는 다른 물리적, 기계적 특성을 갖게 된다. 본 연구에서는 순차적 2차계획법(Sequential Quadratic Programming, SQP)을 이용하는 목표달성기법(Goal-Attainment Method)의 다목적 최적화 기법을 이용하여 활공체 날개의 정적 처짐과 고유진동수 차이를 최소화하는 방법으로 구조모델의 최신화를 수행하였으며, 모드형상의 일치성을 정량적으로 판단하기 위하여 Modal Assurance Criterion(MAC)를 이용하였다.

• 한국융합학회논문지
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• 제4권3호
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• pp.43-50
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• 2013

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

• 한국자동차공학회논문집
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• 제4권1호
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• pp.110-122
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• 1996

The objective of this study is to understand the dynamic characterictics of OHV type valve trains and to design and optimal cam profile which will improve engine performance. A numerical model for valve train dynamics is presented, which aims at both accuracy and computational efficiency. The lumped mass model and distributed parameter model were used to describe the valve train dynamics. Nonlinear characterictics in the valve spring behavior were included in the model. Comprehensive experiments were carried out concerning the valve train dynamics, and the model was tuned based on the test results. The dynamic model was used in designing an optimal cam profile. Because the objective function has many local minima, a conventional local optimizer cannot be used to find an optimal solution. A modified adaptive random search method is successfully employed to solve the problem. Cam lobe area could be increased up to 7.3% without any penalties in kinematic and dynamic behaviors of the valve train.

• Computers and Concrete
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• 제17권6호
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• pp.761-786
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• 2016

This paper proposes an automated procedure for optimum seismic design of reinforced concrete (RC) frame structures. This procedure combines a smart pre-processing using a Tree Classification Method (TCM) and a nonlinear optimization technique. First, the TCM automatically creates sections database and assigns sections to structural members. Subsequently, a real valued model of Particle Swarm Optimization (PSO) algorithm is employed in solving the optimization problem. Numerical examples on design optimization of three low- to high-rise RC frame structures under earthquake loads are presented with and without considering strong column-weak beam (SCWB) constraint. Results demonstrate the effectiveness of the TCMin seismic design optimization of the structures.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• 대한기계학회논문집A
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• 제27권6호
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• pp.864-870
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• 2003

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM(Finite Element Method) and BEM(Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta -modeling technique has been developed for solving such a complex problems combined with the DACE(Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building approximation models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty -six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging model of a train suspension. After each Kriging model is constructed, multi -objective optimal solutions are achieved by using a nonlinear programming method called SQP(Sequential Quadratic Programming).

• 한국경영과학회지
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• 제31권1호
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• 대한기계학회논문집A
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• 제24권6호
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• pp.1633-1642
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• 2000

Like many other mechanical dynamic systems, flexible manipulator systems experience stiction or sticking friction, which may cause input-dependent instabilities. Manipulator performance can be enha nced by identifying friction but it is hard and expensive to measure friction by direct and precise sensing of contact displacements and forces. This study addresses the problem of identifying flexible manipulator joint friction. A dynamic model of a two-link flexible manipulator based upon finite element and Lagrange's method is constructed. The dynamic model includes the effects of joint compliances and actuator dynamics. Friction is also incorporated in the dynamic model to account for stick-slip at the joints. Next, the friction parameters are to be determined. The identification problem is posed as an optimization problem to be solved using nonlinear programming methods. A genetic algorithm is used to increase the convergence rate and the chances of finding the global optimum. The identified friction parameters are experimentally verified and it is expected that the identification technique is applicable to a system parameter identification problem associated with a wide class of nonlinear systems.

• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.383-397
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• 2014

Reverse logistics network design issues have been popularly discussed in recent years. However, few papers in the past literature have been dedicated to incentive effect on return quantity of used products. The purpose of this study is to formulate a dynamic nonlinear programming model of reverse logistics network design with the aim of managing the used products allocation by coordinating the collection centers and recovery facilities to warrant economic efficiency. In the optimization model, a fuzzy approach is applied to interpret the relationship between the rate of return and the suggested incentives. Due to funding constraints in setting up the collection centers, this work considers these centers as multi-capacity levels, which can be opened or closed at different periods. In view of the fact that the problem is known as NP-hard, we propose a heuristic method based on tabu search procedure to solve the presented model. Finally, several dominance properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver.

• Journal of applied mathematics & informatics
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• 제31권5_6호
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• pp.853-867
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• 2013

In this paper a methodology is developed to solve a nonlinear fractional programming problem, whose objective function and constraints are interval valued functions. Interval valued convex fractional programming problem is studied. This model is transformed to a general convex programming problem and relation between the original problem and the transformed problem is established. These theoretical developments are illustrated through a numerical example.