• Journal of the Society of Naval Architects of Korea
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• v.33 no.4
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• pp.162-174
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• 1996

Longline type aquaculture facilities in the open sea are based on the cable-buoy-weight mooring system. For their optimal design it is necessary to estimate tensions along the mooring lines including the attachment points of buoys and weights. However, the dynamic analysis is very complicated due to the nonlinear behaviors of the mooring lines and the effects of wave and current. In this paper, parametric studies for various buoy-weight cases are shown. Finite difference scheme is employed in obtaining eigenfrequencies in the frequency domain. Nonlinear hydrodynamic drag forces are equivalently linearized.

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.23-28
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• 2014

For the reconstruction of three-dimensional (3D) shape of microscopic objects through shape from focus (SFF) methods, usually a single focus measure operator is employed. However, it is difficult to compute accurate depth map using a single focus measure due to different textures, light conditions and arbitrary object surfaces. Moreover, real images with diverse types of illuminations and contrasts lead to the erroneous depth map estimation through a single focus measure. In order to get better focus measurements and depth map, we have combined focus measure operators by using genetic algorithm. The resultant focus measure is obtained by weighted sum of the output of various focus measure operators. Optimal weights are obtained using genetic algorithm. Finally, depth map is obtained from the refined focus volume. The performance of the developed method is then evaluated by using both the synthetic and real world image sequences. The experimental results show that the proposed method is more effective in computing accurate depth maps as compared to the existing SFF methods.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.565-572
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• 2019

Optimization of production system of polyester manufacturing companies is an important task for strengthening the competitiveness of the domestic polyester industry. The purpose of this study is to present a way to evaluate the goodness of the polyester manufacturing systems determined by the combinations of production facilities and to derive the optimal configuration of the production system. To this end, the criteria or factors for the evaluating polyester production system were derived and the Analytic Hierarchy Process (AHP) was used. Using the AHP model, we derived weights on the criteria for evaluating polyester production system and drew priorities for the configurations of the production systems under consideration.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.969-976
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• 2017

This paper proposes the learning method of an artificial neural network and a convolutional neural network using the WFSO algorithm developed as an optimization algorithm. Since the optimization algorithm searches based on a number of candidate solutions, it has a drawback in that it is generally slow, but it rarely falls into the local optimal solution and it is easy to parallelize. In addition, the artificial neural networks with non-differentiable activation functions can be trained and the structure and weights can be optimized at the same time. In this paper, we describe how to apply WFSO algorithm to artificial neural network learning and compare its performances with error back-propagation algorithm in multilayer artificial neural networks and convolutional neural networks.

• Geomechanics and Engineering
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• v.10 no.5
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• pp.689-707
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• 2016

Soil stabilization is one of the useful method of ground improvement for soil with low bearing capacity and high settlement and unrequired swelling potential. Generally, the stabilization is carried out by adding some solid materials. The main objective of this research was to investigate the feasibility of stabilization of organic soils and sewage sludge to obtain low cost alternative embankment material by the addition of two different slags. Slags were used as a replacement for weak soil at ratios of 0%, 25%, 50%, 75% and 100%, where sewage sludge and organic soil were blended with slags separately. The maximum dry unit weights and the optimum water contents for all soil mixtures were determined. In order to investigate the influence of the slags on the strength of sewage sludge and organic soil, and to obtain the optimal mix design; compaction tests, the California bearing ratio (CBR) test, unconfined compressive strength (UCS) test, hydraulic conductivity test (HCT) and pH tests were carried out on slag-soil specimens. Unconfined compressive tests were performed on non-cured samples and those cured at 7 days. The test results obtained from untreated specimens were compared to tests results obtained from soil samples treated with slag. Laboratory tests results indicated that blending slags with organic soil or sewage sludge improved the engineering properties of organic or sewage sludge. Therefore, it is concluded that slag can be potentially used as a stabilizer to improve the properties of organic soils and sewage sludge.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.15-37
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• 2009

This study presents a new retrieval system developed to consider various preferential requirements for buyers in the real estate market. The paper analyses essential factors affecting the price of real estate and then a set of factors are classified by region-related factor and individual-related factor. After endowing the buyer's selected factors with weights in the retrieval system, the optimal solutions have been drawn by comparing with the others through an entropy measure of Multi-attribute Decision Making. This retrieval system is applied to the Busan real estate market to estimate the solutions of retrieval. Evaluation results indicate that the retrieval system can provide useful information to analyse the price determination factors of real estate, as well as to save the searching cost of the buyers.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1451-1456
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• 2013

This paper propose a method for fast convergence technique in frequency domain independent component analysis (FDICA) using eigenanalysis. It important, such as SONAR system, to eliminate the interference sources through fast algorithm. Through eigenanalysing a two-by-two delayed mixture case, information of delay can be used for initial weighting parameters. Simulations show the improved performances in convergence speed and noise rejection rate. The proposed method can present close weights for optimal convergence, noise can be diminished drastically about 3 times epoch, and get the better resultss with 1~3dB than the conventional method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.39-46
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• 2005

In this paper, adaptive higher harmonic control technique using Neural Networks (NN) is proposed. First, linear transfer function is estimated to relate the input harmonics and output harmonics, then NN which has the universal function approximation property is applied to expand application range of the transfer function. Optimal control gain matrix computed from the transfer function is used to train NN weights. Online weight adaptation laws are derived from Lyapunov's direct method to guarantee internal stability. Results of the simulation of 6-input 2-output nonlinear system show that adaptive HHC is applicable to the system with uncertain transfer function.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.22-27
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• 2007

Phosphorylation is one of the most important post translational modifications which regulate the activity of proteins. The problem of predicting phosphorylation sites is the first step of understanding various biological processes that initiate the actual function of proteins in each signaling pathway. Although many prediction methods using single or multiple features extracted from protein sequences have been proposed, systematic data integration approach has not been applied in order to improve the accuracy of predicting general phosphorylation sites. In this paper, we propose an optimal way of integrating multiple features in the framework of multiple kernel learning. We optimally combine seven kernels extracted from sequence, physico-chemical properties, pairwise alignment, and structural information. Using the data set of Phospho. ELM, the accuracy evaluated by 5-fold cross-validation reaches 85% for serine, 85% for threonine, and 81% for tyrosine. Our computational experiments show significant improvement in the performance of prediction relative to a single feature, or to the combined feature with equal weights. Moreover, our systematic integration method significantly improves the prediction preformance compared with the previous well-known methods.