• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.32-42
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• 2021

The printing process can have to print various colors with a limited capacity of printing facility such as ink containers that are needed cleaning to change color. In each container, cleaning time exists to assign corresponding inks, and it is considered as the setup cost required to reduce the increasing productivity. The existing manual method, which is based on the worker's experience or intuition, is difficult to respond to the diversification of color requirements, mathematical modeling and algorithms are suggested for efficient scheduling. In this study, we propose a new type of scheduling problem for the printing process. First, we suggest a mathematical model that optimizes the color assignment and scheduling. Although the suggested model guarantees global optimality, it needs a lot of computational time to solve. Thus, we decompose the original problem into sequencing orders and allocating ink problems. An approximate function is used to compute the job scheduling, and local search heuristic based on 2-opt algorithm is suggested for reducing computational time. In order to verify the effectiveness of our method, we compared the algorithms' performance. The results show that the suggested decomposition structure can find acceptable solutions within a reasonable time. Also, we present schematized results for field application.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.223-230
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• 2015

As the Transportation Simplex Method of the general transportation problem are inapplicable to the large-scale unbalanced transportation problem, a commercialized linear programming package remains as the only viable means. There is, however, no method made available to verify the optimality of solutions attained by the package. This paper therefore proposes a simple heuristic algorithm to the large-scale unbalanced transportation problem. From a given problem of $31{\times}15$supply and demand areas, the proposed algorithm determines the number of demands areas for each supply area and executes on the latter in the ascending order of each of their corresponding demand areas. Next, given a single corresponding demand area, it supplies the full demand volume and else, it supplies first to an area of minimum associated costs and subsequently to the rest so as to meet the demand to the fullest extent. This initial optimal value is then optimized through an adjustment process whereby costs are minimized as much as possible. When tested on the $31{\times}15$cost matrix, the proposed algorithm has obtained an optimal result improved from the commercial linear programming package by 8.9%.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.400-411
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• 2011

Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components-process variables experiments depend on the mixture components-process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. In this paper we propose three starting models for the mixture components-process variables experiments. One of the starting model we are considering is the model which includes product terms up to cubic order interactions between mixture effects and the linear & pure quadratic effect of the process variables from the product model. In this paper, we propose a method for finding robust designs and practical designs with respect to D-, G-, and I-optimality for the various starting combined models and then, we find practically efficient and robust designs for estimating the regression coefficients for those models. We find the prediction capability of those recommended designs in the case of three components and three process variables to be good by checking FDS(Fraction of Design Space) plots.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1060-1065
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• 2005

An Independent Administrative Corporation Japan Agency for Marine-Earth Science and Technology (JAMSTEC) is developing light-and-small Autonomous Underwater Vehicles (AUV)$^{1)}$, named 'MR-X1' (Marine Robot Experimental 1), which can cruise, investigate and observe by itself without human's help. In this paper, we consider the motion control problem of 'MR-X1' and derive a controller. Since the dynamic property of 'MR-X1' is changed by the influence of the speed, the mathematical model of 'MR-X1' becomes the nonlinear model. In order to design a controller for 'MR-X1', we generally apply nonlinear control theories or linear control theories with some constant speed situation. If we design a controller by applying Linear Quadratic (LQ) optimal control theory, the obtained controller only compensates t e optimality at the designed speed situation, and does not compensate the stability at another speed situations. This paper proposes a controller design method using Linear Matrix Inequalities (LMIs)$^{2),3),4)}$, which can adapt the speed variation of 'MR-X1'. And examples of numerical analysis using our designed controller are shown.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.4
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• pp.400-406
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• 2014

In this paper, we propose an approach to minimizing total power consumption by deploying multiple clean-up robots simultaneously in a given area. For this, we propose to use the cooperative game theoretic approaches (i.e., Nash bargaining solution (NBS)) such that the robots can optimally and fairly negotiate the area division based on available resources and characteristics of the area, thereby leading to the minimum total power consumption. We define a utility function that includes power consumptions for characteristics of areas and the robots can agree on a utility pair based on the NBS. Simulation results show that the proposed approach can reduce the total average power consumption by 15-30% compared to a random area division approach.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.963-973
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• 2005

Increasing attention is being recently devoted to various problems inherent in the topological design of networks systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cable. Lately, these network systems are well designed with tiber optic cable, because the requirements from users become increased. But considering the high cost of the fiber optic cable, it is more desirable that the network architecture is composed of a spanning tree. In this paper, we present a GA (Genetic Algorithm) for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable, considering the connection cost, average message delay, and the network reliability We also employ the $Pr\ddot{u}fer$ number (PN) and cluster string in order to represent chromosomes. Finally, we get some experiments in order to certify that the proposed GA is the more effective and efficient method in terms of the computation time as well as the Pareto optimality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4305-4323
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• 2014

In this paper, we investigate the degrees of freedom (DOF) of the Y channel consisting of three single-antenna users and a two-antenna common access relay, where each user intends to exchange independent messages with the other two users with the assistance of the relay. We show that the DOF of this particular scenario is 1.5. In order to prove this result, we firstly derive a DOF upper bound based on cut-set bound by allowing cooperation among users, which shows that the total DOF is upper bounded by 1.5. Then we propose a novel transmission scheme based on asymmetric signal space alignment (ASSA) to demonstrate the achievability of the upper bound. Theoretical evaluation and numerical results confirm that the upper bound can be achieved by utilizing ASSA, which also proves the optimality of the ASSA-based scheme in terms of DOF. Combining the upper bound and achievability, we conclude that the exact DOF is 1.5. Moreover, we present a novel iterative joint beamforming optimization (I-JBO) algorithm to further improve the sum rate. Numerical simulations have been provided to demonstrate the convergence speed and performance advantage of the I-JBO algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.608-616
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• 2007

OFDM Systems for multi-user use adaptive modulation and ending (AMC) which is a method that selects suitable modulation order and code rate depending on channel state of each user. Using AMC, OFDM system can provide high quality and reliable communication. Base station using AMC scheme requires downlink channel information of each terminal to operate optimality. However, under practical system environment, it is unsuitable to transmit all channel information because uplink bandwidth of the system is limited. In this paper, we propose a flexible block-wise loading (FBL) algorithm combined with a novel CQI feedback scheme with reduced number of required bits to optimize the performance of AMC system. Proposed algorithm allocates sub-carrier groups dynamically to improve the sector throughput and outage probability performance.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.