• Journal of Multimedia Information System
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• v.1 no.1
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• pp.87-93
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• 2014

The aim of microscopic image restoration is to recover the image by applying the inverse process of degradation, and the results facilitate automated and improved analysis of the image. In this work, we consider the problem of image restoration as a minimization problem of convex cost function, which consists of a least-squares fitting term and regularization terms with non-negative constraints. The finite step method is proposed to solve this constrained convex optimization problem. We demonstrate the convergence of this method. Efficiency and restoration capability of the proposed method were tested and illustrated through numerical experiments.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.222-224
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• 2006

This paper addresses a particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem(GMS) with some constraints. We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In this paper, we find the optimal solution of the GMS problem within a specific time horizon using particle swarm optimization algorithm. Simple case study with 16-generators system is applicable to the GMS problem. From the result, we can conclude that PSO is enough to look for the optimal solution properly in the generating unit maintenance scheduling problem.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.144-151
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• 2005

We propose an optimization method to reduce the assembly time of chip mounters. Feeder arrangement and assembly sequence are determined considering the multiple feeder assignment. The problem is divided into two sub-problems: feeder arrangement problem and assembly sequence problem. We present mathematical model for each sub-problem. The clustering algorithm and assignment algorithm are applied to solve the feeder arrangement problem. The assignment algorithm and connection algorithm are applied to solve the assembly sequence problem. Simulation results are then presented to verity the usefulness of the proposed method.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.81-86
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• 2005

This paper deals with power system stabilization problem using optimal foraging theory, which formulates foraging as an optimization problem and via computational or analytical methods can provide an optimal foraging policy that specifies how foraging decisions are made. It is possible that the local environment where a population of bacteria live changes either gradually (e.g., via consumption of nutrients) or suddenly due to some other influence. This objective scrutinizes to possibilities for power system stabilization by utilizing how mobile behaviors in both individual and groups of bacteria implement foraging and optimization.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.2
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• pp.38-44
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• 1985

Systems reliability can be improved by using redundancy and/or developing more reliable components. This papaer considers a joint optimization of both alternatives for a series system. It is shown that the n-stage optimization problem can be decomposed into n single stage subproblem. Each subproblem is further transformed into a univariate optimization problem for which a simple and efficient solution method is developed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.254-261
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• 1998

This study presents a discrete optimization of tall steel buildings under multiple drift constraints using a dual method. Dual method can replace the primary optimization problem with a sequence of approximate explicit subproblems. Since each subproblem is convex and separable, it can be efficiently solved by using a dual formulation. Specifically, this study considers the discrete-optimization problem due to the commercial standard steel sections to select member sizes. The results by the proposed method will be compared with those of the conventional optimality criteria method

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.115-119
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• 2007

In [1], robust combinatorial optimization problem is introduced, where a positive integer $\Gamma$ is used to control the degree of robustness. The proposed algorithm needs solutions of n+1 nominal problems. In this paper, we show that the number of problems needed reduces to $n+1-\Gamma$.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.520-525
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• 2024

The optimization design problem of nuclear reactor radiation shield is a typical multi-objective optimization problem with almost 10 sub-objectives and the sub-objectives are always demanded to be under tolerable limits. In this paper, a design method combining multi-objective optimization algorithms with paralleling discrete ordinate transportation code is developed and applied to shield design of the Savannah nuclear reactor. Three approaches are studied for light-weighted and compact design of radiation shield. Comparing with directly optimization with 10 objectives and the single-objective optimization, the approach by setting sub-objectives representing weight and volume as optimization objectives while treating other sub-objectives as constraints has the best performance, which is more suitable to reactor shield design.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.56-63
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• 2016

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.