• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.577-582
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• 1998

In nuclear power plant, it has been the important object to reduce the occupational radiation exposure (ORE). Recently, the optimization concept of management science has been studied to reduce the ORE in nuclear power plant. In optimization of the worker allocation, the collective dose, working time, individual dose, an total number of worker must be considered and their priority orders must be thought because the main constraint is necessary for determining the constraints variable of the radiological worker allocation problem. The ultimate object of this study s to look into the change of the optimal allocation of the radiological worker as priority order changes. In this study, the priority order is the characteristic of goal programming that is a kind of multi-objective linear programming. From a result of study using goal programming, the total number of worker and collective dose of worker have changed as the priority order has changed and the collective dose limit have played an important role in reducing the ORE.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.148-151
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• 2008

In a double-passage cascade apparatus, only two blades are installed in order to increase the accuracy of experimental result by applying bigger blade than the size of multi-blades on the same apparatus. However, this causes difficulties to make correct periodic condition. In this study, sidewalls are designed to meet periodic condition without removing the operating fluid or adjusting tail boards. Surface Mach number on the blade surface is applied to a responsible variable, and 12 design variables which are related with sidewall profile control are selected. A gradient based optimization is adopted for wall design and CFX-11 is used for the internal flow computation. The computed result shows that it could obtain the same flow structure by modifying only the sidewalls of the double-passage cascade apparatus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.565-581
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• 2019

This paper investigates the wireless powered two way relay network (WPTWRN), where two single-antenna users and one single-antenna relay firstly harvest energy from signals emitted by a multi-antenna power beacon (PB) and then two users exchange information with the help of the relay by using their harvested energies. In order to improve the energy transfer efficiency, energy beamforming at the PB is deployed. For such a network, to explore the performance limit of the presented WPTWRN, an optimization problem is formulated to obtain the achievable rate region bounds by jointly optimizing the time allocation and energy beamforming design. As the optimization problem is non-convex, it is first transformed to be a convex problem by using variable substitutions and semidefinite relaxation (SDR) and then solve it efficiently. It is proved that the proposed method achieves the global optimum. Simulation results show that the achievable rate region of the presented WPTWRN architecture outperforms that of wireless powered one way relay network architecture. Results also show that the relay location has significant impact on achievable rate region of the WPTWRN.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.7
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• pp.66-77
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• 1996

Optimal topology design is to search the optimal configuration of a structure which can be used as a shape at the conceptual design stage. Our objective is to maximize the stiffness of the structures and ribs under a material usage constraintl. The density of each finite element is the design variable and its relationship with Young's modulus is expressed by quadratic form. The configuration is represented by the entire density distribution, the structural analysis is performed by finite element method and the optimiza- tion is performed by Feasible Direction Method. Feasible Direction Method can handle various problems simultaneously, that is, mult-objectives and multi-constraints. Total computation time can be reduced by the quadratic relationship between the density and the material property and fewer design variables than Homogenization Method. Toplogy optimization technique developed in this research is applied to design the shapes of the ribs.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.17-23
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• 2008

In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and II weights, has been investigated. The objective is to produce the assigned mixed orders satisfying due dates and obtaining the highest ingot efficiency simultaneously. Implementing simple integer programming instead of complicated genetic algorithms accompanying rigorous calculations proves that it can provide a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Enhancing the ingot efficiency under the constraint of discrete ingot sizes is accomplished by using a simple and intelligible algorithm in a standard integer programming. Employing this simple methodology, a job-shop type foundry is able to maximize the furnace utilization and minimize ingot waste.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.651-652
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• 2008

This research presents a multi-objective optimal design employing topological approach to maximize magnetic energy while minimizing structural deformation which is caused by magnetic reluctance force and magnetostriction. A design sensitivity formula is derived by employing the adjoint variable method (AVM) to avoid numerous sensitivity evaluations for a coupled magneto-mechanical analysis. The sensitivity analysis is verified using the finite difference method (FDM) in a C-shape actuator. A linear actuator used in a home appliance is examined for optimal design and demonstrates the strength of the proposed topology optimization approach.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1589-1590
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• 2006

A method to determine functional relationships between the variable physical dimensions of an antenna and the antenna performance characteristics is presented. By applying multi-expression programming (MEP) to this data set, optimization with regard to a given criteria can be subsequently performed on the functions instead of performing repealed electromagnetic simulations. The functionals are trained on an initial population of simulation samples and refined using a point-wise error estimate to identify design parameters for subsequent samples. Additionally, the depth of the MEP tree is adjusted for increased accuracy as the data set is deemed sufficient.

• 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).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.160-163
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• 2000

Due to the complexities of the real-world system, a water resource management program has to deal with various types of data. It appears that management personnel who has to use the program usually suffers from the technical burdens of handling large amount of data and understanding the optimization theory when they try to interpret the results. By combining the capabilities of database technology and modeling technique with optimization procedure we can develop a reliable decision supporting tool for multi-reservoir operation planning, which yields operating schedule for each dam in a river basin. We introduce two special data handling methodology for the real world application. First, by treating dams, hydro-electric power generating facilities and demand sites as separate database tables, the proposed data handling scheme can be applied to general water resource system in Korea. Second, by assigning variable names using predetermined key words, we can save searching time for identifying the moaning of the variables, so that we can quickly save the results of the optimization run to the database.