• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.3
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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.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.111-126
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• 1999

The article is devoted to mathematical modeling of prob-lems of stochastic optimization of the plastic metal working. Classifi-cation and mathematical statements of such problems are proposed. Several calculation techniques of the single goal function are pre-sented. The probability theory and the Fuzzy numbers were applied for solution of the problems of stochastic optimization.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1328-1333
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• 2003

In the present study, an integrated framework of geometric modeling, analysis, and design optimization is proposed. Geometric modeling is based on B-spline surface representation. Geometrically-exact shell finite element is implemented in analysis module. Control points of the surface are selected as design variables for optimization, which can make the interaction easier between analysis and surface representation. Sequential linear programming(SLP) is adopted for the shape optimization of surfaces. For the computation of shape sensitivities, semi-analytical method is used. The developed integrated framework should serve as a powerful tool for the geometric modeling, analysis, and shape design of surfaces.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.29-40
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• 2006

The paper presents an optimization system which integrates a parametric design tool, 3D diffraction-radiation analysis and hydrodynamic performance assessment based on short and long term wave statistics. Controlled by formal optimization strategies the system is able to design offshore structure hulls with superior seakeeping qualities. The parametric modeling tool enables the designer to specify the geometric characteristics of the design from displacement over principal dimensions down to local shape properties. The computer generates the hull form and passes it on to the hydrodynamic analysis, which computes response amplitude operators (RAOs) for forces and motions. Combining the RAOs with short and long-term wave statistics provides a realistic assessment of the quality of the design. The optimization algorithm changes selected shape parameters in order to minimize forces and motions, thus increasing availability and safety of the system. Constraints ensure that only feasible designs with sufficient stability in operation and survival condition are generated. As an example the optimization study of a semisubmersible is discussed. It illustrates how offshore structures can be optimized for a specific target area of operation.

• Food Science of Animal Resources
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• v.43 no.2
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• pp.374-381
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• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.507-516
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• 2000

In the present paper we consider a problem of choosing the rational way to carry on the metal processing (the problem of stochastic optimization) and the problem of determing the unknown characteristics of parameters described with random variables.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.1
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• pp.1-15
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• 1998

Currently, computational analysis is a popular technology in automobile engineering. Finite element analysis is an excellent method for body analysis. For finite element analysis, accurate modeling is very important to obtain precise information. Stick modeling is a convenient way in that it is easy and simple. When a stick model is utilized, the joints are modified in the tuning process. A tuning method for the joint has been developed. The joints are modeled by designated beam elements. An optimization method called "Goal Programming" is employed to impose the target values. With the tuned joints, the entire optimization has been carried out. Using the "Recursive Quadratic Programming" algorithm, the optimization process determines the configuration of the entire structure and sizes of all the sections. For example, the structure of an electrical vehicle is modeled and analyzed by the method. The stick model works well since the structure is made of aluminium frames. Although the example handles an electrical vehicle, this method can be applied to general vehicle structures.

• YAKHAK HOEJI
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• v.59 no.4
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• pp.151-157
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• 2015

The purpose of this study is to demonstrate how lead compounds are best optimized with the application of in silico QSAR and PBPK modeling at the early drug discovery stage. Several predictive QSAR models such as $IC_{50}$ potency model, intrinsic clearance model and brain penetration model were built and applied to a set of virtually synthesized library of the BACE1 inhibitors. Selected candidate compounds were also applied to the PBPK modeling for comparison between the predicted animal pharmacokinetic parameters and the observed ones in vivo. This novel lead optimization strategy using QSAR and PBPK modelings could be helpful to expedite the drug discovery process.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.