• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.11 s.242
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• pp.1445-1454
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• 2005

In order to minimize warping deformation which is an essential factor in the failure of injection molding parts, this study proposes an optimization design method fer determining design variables of injection molding parts. First, using a commercial package program for injection molding analysis, namely, Computer Aided Plastics Application(CAPA), we investigate the effects of parameters of injection molding process. Next, an optimum design process is established by interfacing CAPA to PQRSM embedded in EMD10S, a design framework developed by the conte. of innovative Design Optimization Technology(iDOT). PQRSM is a very efficient sequential approximate optimization algorithm. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the results of the optimum design is better than those of the initial design. It is believed that the proposed methodology can be applied to other injection molding design applications.

• The KSFM Journal of Fluid Machinery
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• v.12 no.4
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• pp.44-53
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• 2009

Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1219-1224
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• 2004

In this study the optimization of plate-fin type heat sink with vortex generator for thermal stability is conducted numerically. To acquire the optimal design variables, the CFD and mathematical optimization are integrated. The flow and thermal fields are predicted using the finite volume method. The optimization is carried out by means of the sequential quadratic programming (SQP) method. The results show that when the temperature rise is less than 40 K, the optimal design variables are as follows; $B_1=2.584mm$, $B_2=1.741mm$, and t = 7.914 mm. Comparing with the initial design, the temperature rise is reduced by 4.2 K, while the pressure drop is increased by 9.43 Pa. The Pareto optimal solutions are also presented between the pressure drop and the temperature rise.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1169-1182
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• 2006

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.736-743
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• 2016

This study describes optimization methods for adjustment of helicopter main rotor tracking and balancing (RTB). RTB is a essential process for helicopter operation and maintenance. Linear and non-linear models were developed with past RTB test results for estimation of RTB adjustment. Then global and sequential optimization methods were applied to the each of models. Utilization of the individual optimization method with each model is hard to fulfill the RTB requirements because of different characteristics of each blade. Therefore an ensemble model was used to integrate every estimated adjustment result, and an adaptive method was also applied to adjustment values of the linear model to update for next estimations. The goal of this developed RTB adjustment optimization program is to achieve the requirements within 2 run. Additional tests for comparison of weight factor of the ensemble model are however necessary.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.2
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• pp.206-213
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• 2004

In this study, the topology optimization is applied to the design of a piezoelectric microactuator satisfying the specific mean transduction ratio(MTR). The optimization problem is formulated to minimize the difference between the specified and the current mean transduction ratio. In order to analyze the response of the piezoelectric-structure coupled system, both the structural and the electric potential are considered in the finite element method. The optimization problem is resolved by using Sequential Linear Programming(SLP) and the results of test problems show that the design of a piezoelectric microactuator with the specified mean transduction ratio can be obtained.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.257-266
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• 2012

This paper overviews clock design problems related to the circuit reliability in deep submicron design technology. The topics include the clock polarity assignment problem for reducing peak power/ground noise, clock mesh network design problem for tolerating clock delay variation, electromagnetic interference aware clock optimization problem, adjustable delay buffer allocation and assignment problem to support multiple voltage mode designs, and the state encoding problem for reducing peak current in sequential elements. The last topic belongs to finite state machine (FSM) design and is not directly related to the clock design, but it can be viewed that reducing noise at the sequential elements driven by clock signal is contained in the spectrum of reliable circuit design from the clock source down to sequential elements.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1204-1209
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• 1990

This paper offers the theory and method for regression analysis of the regression model with operational parameter variables based on the fundamentals of mathematical statistics. Regression coefficients are usually constants related to the problem of regression analysis. This paper considers that regression coefficients are not constants but the functions of some operational parameter variables. This is a kind of method of two-step fitting regression model. The second part of this paper considers the experimental step numbers as recursive variables, the recursive identification with unknown operational parameter variables, which includes two recursive variables, is deduced. Then the optimization and the recursive identification are combined to obtain the sequential experiment optimum design with operational parameter variables. This paper also offers a fast recursive algorithm for a large number of sequential experiments.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.318-324
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• 2006

The mobile manipulator is expected to work in partially defined or unstructured environments. In our global/local approach to path planning, joint trajectories are generated for a desired Cartesian space path, designed by the global path planner. For a local path planner, inverse kinematics for a redundant system is used. Joint displacement limit for the manipulator links is considered in the motion planner. In an event of failure to obtain feasible trajectories, the task cannot be accomplished. At the point of failure, a deviation in the Cartesian space path is obtained and a replanner gives a new path that would achieve the goal position. To calculate the deviation, a nonlinear optimization problem is formulated and solved by standard Sequential Quadratic Programming (SQP) method.