• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.04a
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• pp.305-312
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• 1999

This study is intended to propose a systematic and practical life cycle cost(LCC) model for the development of the reliability-based seismic safety and cost-effective performance criteria for design and upgrading of long-span PC bridges. The LCC models consist of five cost functions such as initial cost, repair/replacement cost, human losses, road user cost, and indirect losses of regional economy. The proposed model Is successfully expressed in temrs of Park-Ang damage indices and life cycle damage probability obtained from SMART-DRAIN-2DX which is an existing algorithm for nonlinear time history analysis. The proposed LCC model is successfully applied to a viaduct constructed by PSM, in Seoul. Based on the observations, the proposed systematic procedure for the formulation of LCC model may be useful for the development of the reliability-based seismic safety and cost-effective performance criteria for design and upgrading of long-span PC bridges.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.10a
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• pp.288-295
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• 1996

This work presents an optimality criteria method applicable io the design of plane frames with I-shape sections. All kinds of constraints are treated properly to ensure the mathematical rigour of the method as ever. Among the various properties of a section, the cross-sectional area is chosen as the design variable associated with the member. Then other properties, moment of inertia and depth, are determined from the cross-sectional area using relationships established in advance from the sectional data for AISC standard W shapes. The optimality criteria established in this work is perfect in mathematical terms provided that the relationships between properties of a section are correct. A redesign algorithm is derived relying heavily on the Newton-Raphson method to solve the system of nonlinear constraint equations. A worked example is also Presented.

• Journal of Ship and Ocean Technology
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• v.11 no.4
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• pp.21-28
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• 2007

Recently the importance of the utilization of engineering data is gradually increasing. Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper deals with generating optimal polynomials using genetic programming (GP) as the module of Data Mining system. Low order Taylor series are used to approximate the polynomial easily as a nonlinear function to fit the accumulated data. The overfitting problem is unavoidable because in real applications, the size of learning samples is minimal. This problem can be handled with the extended data set and function node stabilization method. The Data Mining system for the ship design based on polynomial genetic programming is presented.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.974-976
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• 1996

In this paper, a reference model following control system using a fuzzy logic controller(FLC) is proposed By using an integrator and a nonlinear hysteresis element, a reference model whose response has no overshoot and fast rise time is designed. A FLC is designed to follow as close as possible to the response of the reference model. The proposed design method is shown that the robustness and the optimal tracking property can be achieved under modeling error, disturbance and parameter perturbations. The effectiveness of the proposed design method is verified through the simulation that compare using the FLC with using a $H_{\infty}$ controller.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.915-917
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• 2003

In this paper, a 3D shape optimization algorithm which guarantees a smooth optimal shape is presented using parameterized sensitivity analysis. The design surface is parameterized using Bezier spline and the control points of the spline are taken as the design variables. The parameterized sensitivity for the control points are found from that for nodal Points. The design sensitivity and adjoint variable formulae are also derived for the 3D non-linear problems. Through an application to the shape optimization of 3D electromagnet to get a uniform magnetic field, the effectiveness of the proposed algorithm is shown.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1029-1038
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• 2014

Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.66-72
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• 2000

As the demand for the design of modular manipulators or special purpose manipulators has increased, task based design to design an optimal manipulator for a given task become more and more important. However, the complexity with a large number of design parameters, and highly nonlinear and implicit functions are characteristics of a general manipulator design. To achieve the goal of task based design, it is necessary to develop a methodology to solve the complexity. This paper addresses how to determine the kinematic parameters of a two-degrees of freedom manipulator with parallelogram five-bar link mechanism from a given task, namely, how to map a given task into the kinematic parameters. With simplified example of designing a manipulator with five-bar link mechanism, the methodology for task based design is presented. And it introduces formulations of a given task and manipulator specifications, and presents a new dexterity measure for manipulator design. Also the optimization problem with constraints is solved by using a genetic algorithm that provides robust search in complex spaces.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.90-96
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• 2014

This paper presents a practical linearization technique for a wide-band bang-bang digital phase locked-loop(BBDPLL) by selecting optimal loop gains. In this paper, limitation of the theoretical design method for BBDPLL is explained, and introduced how to implement practical BBDPLLs with CMOS process. In the proposed BBDPLL, the limited cycle noise is removed by reducing the proportional gain while increasing the integer array and dither gain. Comparing to the conventional BBDPLL, the proposed one shows a small area, low power, linear characteristic. Moreover, the proposed design technique can control a loop bandwidth of the BBDPLL. Performance of the proposed BBDPLL is verified using CppSim simulator.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.