• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.223-226
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• 2002

If one can easily modify the existing motion data to a new motion in making an animation movie, he can save a lot of time for graphic design. To implement this kind of system, we propose a PC-based system composed of low cost commercial animation tool (3D Studio Max) for visualization of the animation and motion editing module that handles optimization process during the motion transform. Researchers studying advanced motion transform techniques only have to focus on the mathematical manipulation of the motion data

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.122-125
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• 2000

The paper describes the adaptation of evolutionary fuzzy model ins (EFM) in developing global function approximation tools for use in genetic algorithm based optimization of nonlinear structural systems. EFM is an optimization process to determine the fuzzy membership parameters for constructing global approximation model in a case where the training data are not sufficiently provided or uncertain information is included in design process. The paper presents the performance of EFM in terms of numbers of fuzzy rules and training data, and then explores the EFM based sizing of automotive component for passenger protection.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1769-1774
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• 2009

An algorithm for extracting the BJT DC model parameter values for SPICE model is proposed. The nonlinear optimization method for analyzing the device I-V data using the Levenberg-Marquardt algorithm is proposed and the method for calculating initial conditions of model parameters to improve the convergence characteristics is proposed. The base current and collector current obtained from the proposed method shows the root mean square error of 6.04% compared with the measured data of the PNP BJT named 2SA1980.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.377-390
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• 2011

A common problem encountered in product or process design is the selection of optimal parameter levels which involves simultaneous consideration of multiple response variables. This is called a multiresponse problem. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. Up to date, various methods have been proposed for the optimization, including the desirability function approach and loss function approach. In this paper, the existing studies in multiresponse optimization are reviewed and a future research direction is then proposed.

• Bulletin of the Korean Mathematical Society
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• v.51 no.1
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• pp.287-301
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• 2014

In this paper, we prove a necessary optimality theorem for a nonsmooth optimization problem in the face of data uncertainty, which is called a robust optimization problem. Recently, the robust optimization problems have been intensively studied by many authors. Moreover, we give examples showing that the convexity of the uncertain sets and the concavity of the constraint functions are essential in the optimality theorem. We present an example illustrating that our main assumptions in the optimality theorem can be weakened.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.370-375
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• 2005

This paper describes a systematic way of identifying the horizontal coefficient of consolidation for clayey soil under undrained condition and silty soil under partial drained condition by applying an optimization technique to the early part of dissipation data measured from the self-boring pressuremeter strain holding test. An analytical solution developed by Randolph & Wroth (1979) was implemented in normalized form to express the build-up and dissipation of excess pore pressures around a pressuremeter as a function of the rigidity index. Horizontal coefficient of consolidation was determined by minimizing the differences between theoretical and measured excess pore pressure curves using optimization technique. It was found that the proposed optimization technique can evaluate in-situ horizontal coefficient of consolidation rationally, which is similar with that obtained from the piezocone dissipation test. Furthermore, proposed method can evaluate appropriate coefficient of consolidation for soil under partially drained condition.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.5
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• pp.245-249
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• 2006

This study tries to test and analyze the static friction torque generated by an electromagnetic clutch. Then the torque is improved by optimizing the shape of armature. For the purpose of design change and optimization of the electromagnetic clutch, the static friction torque prediction is very important. We construct an axi symmetric FEM model for analyzing the static friction torque and used a torque tester for evaluating the real torque. For a test, predicted static friction torque is compared with the experimental one to discuss the rationality of torque analysis process. The analytical result agrees well with experimental data, explaining the validity of the mathematical process and FEM model. After confirming the torque analysis process, the optimization process is investigated. The optimization result shows that the static torque is improved by changing the armature shape.