• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.745-745
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• 1995

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.469-471
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• 1999

In this paper, a dynamic output feedback controller design technique for robust decentralized stabilization of uncertain large-scale systems is presented. Based on the Lyapunov method, a sufficient condition for robust stability, is derived in terms of three linear matrix inequalities(LMIs). The solutions of the LMIs can be easily obtained using efficient convex optimization techniques.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2293-2295
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• 2000

This paper describes the synthesis of robust decentralized controllers for uncertain large-scale discrete-time systems with time-delays in subsystem interconnections. Based on the Lyapunov method, a sufficient condition for robust stability, is derived in terms of a linear matrix inequality(LMI). The solutions of the LMI can be easily obtained using various efficient convex optimization techniques. A numerical example is given to illustrate the proposed method.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.513-520
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• 2018

Since Paris Climate Change Conference in 2015, many policies to reduce the emission of greenhouse gas have been accelerating, which are mainly related to renewable energy resources and micro-grid. Presently, the technology development and demonstration projects are mostly focused on diversifying the power resources by adding wind turbine, photo-voltaic and battery storage system in the island-type small micro-grid. It is expected that the large-scaled micro-grid projects based on the regional district and town/complex city, e.g. the block type micro-grid project in Daegu national industrial complex will proceed in the near future. In this case, the economic cost or the carbon emission can be optimized by the efficient operation of energy mix and the appropriate construction of electric and heat supplying facilities such as cogeneration, renewable energy resources, BESS, thermal storage and the existing heat and electricity supplying networks. However, when planning a large residential town or city, the concrete plan of the energy infrastructure has not been established until the construction plan stage and provided by the individual energy suppliers of water, heat, electricity and gas. So, it is difficult to build the efficient energy portfolio considering the characteristics of town or city. This paper introduces an energy mix optimization(EMO) method to determine the optimal capacity of thermal and electric resources which can be applied in the design stage of the real large-scaled residential town or city, and examines the feasibility of the proposed method by applying the real heat and electricity demand data of large-scale residential towns with thousands of households and by comparing the result of HOMER simulation developed by National Renewable Energy Laboratory(NREL).

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.117-127
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• 2000

In this paper, we propose a new convex combination weight rule for the cross decomposition method which is known to be one of the most reliable and promising strategies for the large scale optimization problems. It is called generalized cross decomposition, a modification of linear mean value cross decomposition for specially structured linear programming problems. This scheme puts more weights on the recent subproblem solutions other than the average. With this strategy, we are having more room for selecting convex combination weights depending on the problem structure and the convergence behavior, and then, we may choose a rule for either faster convergence for getting quick bounds or more accurate solution. Also, we can improve the slow end-tail behavior by using some combined rules. Also, we provide some computational test results that show the superiority of this strategy to the mean value cross decomposition in computational time and the quality of bounds.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.117-122
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• 2016

Optimizing the number of ad clicks is a large-scale learning problem that is central to the multi-billion dollar mobile advertising industry. There are currently several optimization methods used, including ad mediation and ad positioning. This paper proposes a new method to optimize mobile advertising by using the ad refresh interval. A new metric, which can measure and compare mobile advertising performance, takes into account time limitations. The results achieved from this optimization study could maximize revenue for mobile advertisers and publishers. This research has high applicability. It also lays out a solid background for future research in this promising area.

• Computational Structural Engineering
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• v.5 no.1
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• pp.133-142
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• 1992

This paper presents a method to apply the linear goal programming, which has rarely been used to the structural opimization problem due to its unique formulation, to large scale nonlinear structural optimization. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty in defining an objective function and constraints. The method uses the finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the minimum weight structural optimization problems with stress constraints are solved for the cases of 10, 25 and 200 trusses and compared with the results of the other works.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.51-58
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• 2015

This paper suggests assignment-swap algorithm with time complexity O(mn) to obtain the optimal solution for large-scale of transportation problem (TP) with incomplete cost lists. Generally, the TP with complete cost lists can be solved with TSM (Transportation Simplex Method). But, we can't be solved for large-scale of TP with TSM. Especially. It is hard to solve for large-scale TP with incomplete cost lists using TSM. Therefore, experts simply using commercial linear programming package. Firstly, the proposed algorithm applies assignment strategy of transportation quantity to ascending order of transportation cost. Then, we reassign from surplus of supply to shortage of demand. Secondly, we perform the 2-opt and 1-opt swap optimization to obtain the optimal solution. Upon application to $31{\times}15$ incomplete cost matrix problem, the proposed assignment-swap algorithm more improves the solution than LINGO of commercial linear programming.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.129-134
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• 2008

Ultrasonic cavitational energy distributions were measured in a large-scale sonoreator. In application of 110 and 170 kHz of ultrasound, the cavitational energy was just detected near the transducer module. However 35 and 72 kHz ultrasound made good distributions from the module to the end of the sonoreactor, Especially, 72 kHz ultrasound application showed most stable and highest cavitational energy value through the whole length. In the comparison between input power and cavitational energy, linear relationships were obtained in 35 and 72 kHz and it was anticipated that these results would be used for the optimization of input power for the design of sonoreactors. And three dimensional energy distribution was depicted through the mapping of cavitaional energy. Average energy in the large-scale sonoreactor was estimated as 62.8 W, which was about 40 % of input power.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.612-620
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• 2017

Relay coordination in power system is a complex problem and so far, meta-heuristic algorithms and other methods as an alternative approach may not properly deal with large scale relay coordination due to their huge time consuming computation. In some cases the relay coordination could be unachievable. As the urgency for a proper approach is essential, in this paper an innovative and simple relay coordination method is introduced that is able to be applied on optimization algorithms for relay protection coordination. The objective function equation of operating time of relays are divided into two separate functions with less constraints. As the analytical results show here, this equivalent method has a remarkable speed with high accuracy to coordinate directional relays. Two distribution systems including directional overcurrent relays are studied in DigSILENT software and the collected data are examined in MATLAB. The relay settings of this method are compared with particle swarm optimization and genetic algorithm. The analytical results show the correctness of this mathematical and practical approach. This fast coordination method has a proper velocity of convergence with low iteration that can be used in large scale systems in practice and also to provide a feasible solution for protection coordination in smart grids as online or offline protection coordination.