• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.18-27
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• 2006

The conventional reliability based design optimization(RBDO) methods require high computational cost compared with the deterministic design optimization(DO) methods, therefore it is hard to apply directly to large-scaled problems such as an aerodynamic shape design optimization. In this study, to overcome this computational limitation the efficient RBDO procedure with the two-point approximation(TPA) and adjoint sensitivity analysis is proposed, that the computational requirement is nearly the same as DO and the reliability accuracy is good compared with that of RBDO. Using this, the 3-D aerodynamic shape design optimization is performed very efficiently.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.173-183
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• 2018

This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the $P-{\Delta}$ effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

• Advances in Computational Design
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• v.7 no.1
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• pp.1-17
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• 2022

In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.5
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• pp.94-99
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• 2010

This paper presents a design procedure of digitally controlled oscillator(DCO) for power optimization. By controlling coarse tuning bits and fine tuning bits of DCO, the proposed design procedure can optimize the power dissipation and does not affect the LSB resolution, frequency range, linearity, portability. For optimization, the relationship between control bits and power dissipation of the DCO was analyzed. The DCO circuits using and unusing proposed design technique have been designed, simulated and proved using 0.13um, 1.2V CMOS library. The DCO circuit with proposed design technique has operation range between 283MHz and 1.1GHz and has 1.7ps LSB resolution and consumes 2.789mW at frequency of 1GHz.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.211-218
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• 2010

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. Moreover, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and design (control) factors, and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network without resorting to SN. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1117-1124
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• 2010

PMIPv6 is a network-based mobility support scheme, proposed and standardized by NetLMM WG of IETF. It is proposed to solve problems of conventional mobility schemes, and to improve inefficiency of those. The standard document describes network components and detailed procedures to provide mobility to MN. But it describes only a handover procedure between MAGs, not between LMAs. In order to support seamless connectivity of MN efficiently, a handover procedure between LMAs is necessary. The proposed scheme in this paper utilizes a route optimization procedure to prevent inefficiency of inter-LMA tunneling scheme. At the same time, the proposed scheme utilizes a pre-authentication scheme to reduce handover latency. According to the result of performance evaluations, the proposed scheme greatly reduces handover latency, compared to conventional mobility support schemes.

• Computers and Concrete
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• v.23 no.6
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• pp.433-444
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• 2019

As rice husk ash (RHA) is not produced in controlled manufacturing process like cement, its properties vary significantly even within the same lot. In fact, properties of Rice Husk Ash Based Concrete (RHABC) are largely dictated by uncertainty leading to huge deviations from their expected values. This paper proposes a Robust Cost Optimization (RCO) procedure for RHABC, which minimizes such unwanted deviation due to uncertainty and provides guarantee of achieving desired strength and workability with least possible cost. The RCO simultaneously minimizes cost of RHABC production and its deviation considering feasibility of attaining desired strength and workability in presence of uncertainty. RHA related properties have been modeled as uncertain-but-bounded type as associated probability density function is not available. Metamodeling technique is adopted in this work for generating explicit expressions of constraint functions required for formulation of RCO. In doing so, the Moving Least Squares Method is explored in place of conventional Least Square Method (LSM) to ensure accuracy of the RCO. The efficiency by the proposed MLSM based RCO is validated by experimental studies. The error by the LSM and accuracy by the MLSM predictions are clearly envisaged from the test results. The experimental results show good agreement with the proposed MLSM based RCO predicted mix properties. The present RCO procedure yields RHABC mixes which is almost insensitive to uncertainty (i.e., robust solution) with nominal deviation from experimental mean values. At the same time, desired reliability of satisfying the constraints is achieved with marginal increment in cost.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2666-2671
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• 2007

This work presents microchannel heat sink shape optimization procedure using Kriging method. Design variables relating to microchannel width, depth and fin width are selected, and thermal resistance has been taken as objective function. Design points are selected through a three-level fractional factorial design of sampling method. Navier-Stokes and energy equations for laminar flow and conjugate heat transfer are solved at these design points using a finite volume solver. Solutions are carefully validated with experimental results. Using the numerically evaluated objective function, a surrogate model (Kriging) is constructed and optimum point is searched by sequential quadratic programming. The process of shape optimization greatly improves the thermal performance of microchannel heat sink under constant pumping power.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.7 s.250
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• pp.700-706
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• 2006

This study presents a numerical procedure to optimize the shape of dimple surface to enhance turbulent heat transfer in a rectangular channel. The response surface based optimization method is used as an optimization technique with Reynolds-averaged Wavier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter ratio, channel height-to-dimple print diameter ratio, and dimple print diameter-to-pitch ratio are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. full factorial method is used to determine the training points as a mean of design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.