• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3329-3350
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• 2017

Installing caching and computing facilities in mobile edge networks is a promising solution to cope with the challenging capacity and delay requirements imposed on future mobile communication systems. The problem of optimal facility placement in mobile edge networks has not been fully studied in the literature. This is a non-trivial problem because the mobile edge network has a unidirectional topology, making existing solutions inapplicable. This paper considers the problem of optimal placement of a fixed number of facilities in a mobile edge network with an arbitrary tree topology and an arbitrary demand distribution. A low-complexity sequential algorithm is proposed and proved to be convergent and optimal in some cases. The complexity of the algorithm is shown to be $O(H^2{\gamma})$, where H is the height of the tree and ${\gamma}$ is the number of facilities. Simulation results confirm that the proposed algorithm is effective in producing near-optimal solutions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.946-949
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• 2005

It is very a time-consuming and error-prone process to obtain the optimal injection condition, which can produce good injection molding products in some operational variation of facilities, from a seed injection condition. This study proposes a new approach to search the optimal injection molding condition using a neural network and a genetic algorithm. To estimate the defect type of unknown injection conditions, this study forces the neural network into learning iteratively from the injection molding conditions collected. Major two parameters of the injection molding condition - injection pressure and velocity are encoded in a binary value to apply to the genetic algorithm. The optimal injection condition is obtained through the selection, cross-over, and mutation process of the genetic algorithm. Finally, this study compares the optimal injection condition searched using the proposed approach. with the other ones obtained by heuristic algorithms and design of experiment technique. The comparison result shows the usability of the approach proposed.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.80-89
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• 2009

A fast and efficient search algorithm to determine an optimal number of clusters in clustering algorithms is presented. The method is based on cluster validity index which is a measure for clustering optimality. As the clustering procedure progresses and reaches an optimal cluster configuration, the cluster validity index is expected to be minimized or maximized. In this Paper, a fast non-exhaustive search method for finding the optimal number of clusters is designed and shown to work well in clustering. The proposed algorithm is implemented with the k-mean++ algorithm as underlying clustering techniques using CB and PBM as a cluster validity index. Experimental results show that the proposed method provides the computation time efficiency without loss of accuracy on several artificial and real-life data sets.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.457-460
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• 2005

This paper proposes a new Interior Point Method algorithm to improve the computation speed and solution stability, which have been challenging problems for employing the nonlinear Optimal Power Flow. The proposed algorithm is different from the tradition Interior Point Methods in that it adopts the Predictor-Corrector Method. It also accommodates the five minute dispatch, which is highly recommenced in modern electricity market. Finally, the efficiency and applicability of the proposed algorithm is demonstrated with a case study.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.554-558
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• 2001

In this study, a hybrid method is used to search the pseudo-optimal solution for the I-dimentional nesting problem. This method is composed of the genetic algorithm for the global search and a simple heuristic one for the local search near the pseudo optimal solution. Several simulation results show that the hybrid method gives very satisfactory results.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.59-65
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• 1992

A modified primal-dual affine scaling algorithm for linear programming is presented. This modified algorithm generates an elipsoid containing all optimal dual solutions at each iteration, then checks whether or not a dual hyperplane intersects this ellipsoid. If the dual hyperplane has no intersection with this ellipsoid, its corresponding column must be optimal nonbasic. By condensing these columns, the size of LP problem can be reduced.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.1-6
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• 1993

In this paper, we observe that the design of optimal weighted order statistic(WOS) filters minimizing the mean absolute error criterion can be though of as a two-class linear classification problem. Based on this observation, the perceptron algorithm is applied to design WOS filters. It is shown, through experiments, that the perceptron algorithm can find optimal or near optimal WOS filters in practical situations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.33-41
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• 1999

The original tolerances, which are assigned by designers on the basis of handbooks and experience, cannot always be expected to be optimal or feasible, because they may yield an unacceptable manufacturing costs. So the systematic tolerance design considering manufacturing costs should be done. Therefore, this research analyzes the tolerance within the tolerance design using Monte-Carlo simulation method and sensitivity analysis and using genetic algorithm by tolerance allocation method. The genetic algorithm was developed for allocation of the optimal tolerance under the manufacturing limitation cost.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.373-374
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• 2020

This paper proposes an optimal control algorithm which determines active damping resistor values considering load variation and grid side current THD. Proposed optimal control algorithm improves grid side current THD of the interlinking converter without passive damping resistor and is verified by simulation under variable load conditions.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.