• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.13-16
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• 2000

In this paper, we propose hybrid control method of neural network using the 3-point search algorithm. Proposed control method is searched the weight using the 3-point search algorithm for off-line then control the on-line. In order to verify the usefulness of the proposed method, we simulated the proposed control method with one link manipulator system and confirmed the excellency.

• 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.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.366-369
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• 1991

Load shedding algorithm has been developed using heuristic search and D.C flow method. The line flow error with a D.C method was improved in this paper. Minimization of load shedding was obtained with heuristic search method. Although analytical method have been mixed with expert algorithm the C language fitted well for this purpose.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.222-226
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• 2003

Information retrieval must be able to search the most suitable document that user need from document set. If foretell document adaptedness by similarity degree about QL(Query Language) of document, documents that search person does not require are searched. In this paper, showed that can search the most suitable document on user's request searching document of the whole space using genetic algorithm and used knowledge-base operator to solve various model's problem.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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• pp.1091-1097
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• 2005

In this paper, we propose fast motion estimation algorithm. Local statistics of a motion vector is highly correlated to motion vectors of its neighboring blocks. According to the property, block-based motion search range is adaptively determined in order to reduce unnecessary search points. Based on the determined search range, motion vector is obtained by variable step search motion estimation. Experimental results show that comparing to Full search motion estimation, the motion searching points of proposed algorithm is reduced as much as $98\%$. Moreover, PSNR and Bit Rate are almost same to Full search method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.637-642
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• 2014

In this paper, we propose a efficient shift index searching algorithm for design of the block LDPC codes. It is combined with the message-passing based cycle search algorithm and ACE algorithm. We can determine the shift indices by ordering of priority factors which are effect on the LDPC code performance. Using this algorithm, we can construct the LDPC codes with low complexity compare to trellis-based search algorithm and save the memory for storing the parity check matrix.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1367-1376
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• 1998

In this paper we propose an real time marker detection algorithm for motion analysis both in 2 dimensions and 3 dimensions with CCD camera and rfame grabber only which has no image processor. The main algorithm consists of the following 3 algorithms; 1) the tracing algorithm that makes it possible to predict the expected marker location by narrowing the searching boundary, 2) the searching algorithm that detects the marker in the expected boundary using Ad-hoc previous screen search technique, tornado search method rotate diagonal search method search technique, 3) the algorithm that finds the central point of the detected marker. We try to narrow the searching boundary for real time processing. Also, it is able to find the central point of the detected marker much faster than typical contour tracing algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.843-859
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• 2024

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.