• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

• Journal of Korean Society on Water Environment
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• v.38 no.2
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• pp.95-102
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• 2022

Studies for improving the efficiency of the traditional anaerobic digestion process are being actively conducted. To improve anaerobic digestion efficiency, this study tried to derive the optimal pretreatment conditions and mixing conditions by integrating the heat solubilization pretreatment of sewage sludge, livestock manure, and food waste. The soluble chemical oxygen demand (SCOD) increase rate of sewage sludge before and after heat solubilization pretreatment showed an increased rate of 224.7% compared to the control group at 170℃ and 25 min and showed the most stable increase rate. As a result of the biomethane potential test of sewage sludge before and after heat solubilization pretreatment, the total chemical oxygen demand (TCOD) and SCOD removal rates increased as the heat solubilization temperature increased, but did not increase further at temperatures above 170℃. In the case of methane generation, there was no significant change in the cumulative methane generation from 0.134 to 0.203 Sm³-CH₄/kg-COD at 170℃ for 15 min. As a result of the integrated digestion of organic waste, the experimental condition in which 25% of the sewage sludge, 50% of the food waste, and 25% of the livestock manure were mixed showed the highest methane production of 0.3015 m³-CH₄/kg-COD, confirming that it was the optimal mixing ratio condition. In addition, under experimental conditions mixed with all three substrates, M4 conditions mixed with 25% sewage sludge, 50% food waste, and 25% livestock manure showed the highest methane generation at 0.2692 Sm³-CH₄/kg-COD.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Steel and Composite Structures
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• v.29 no.6
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• pp.749-758
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• 2018

This article derived a hybrid coupling technique using the higher-order displacement polynomial and three soft computing techniques (teaching learning-based optimization, particle swarm optimization, and artificial bee colony) to predict the optimal stacking sequence of the layered structure and the corresponding frequency values. The higher-order displacement kinematics is adopted for the mathematical model derivation considering the necessary stress and stain continuity and the elimination of shear correction factor. A nine noded isoparametric Lagrangian element (eighty-one degrees of freedom at each node) is engaged for the discretisation and the desired model equation derived via the classical Hamilton's principle. Subsequently, three soft computing techniques are employed to predict the maximum natural frequency values corresponding to their optimum layer sequences via a suitable home-made computer code. The finite element convergence rate including the optimal solution stability is established through the iterative solutions. Further, the predicted optimal stacking sequence including the accuracy of the frequency values are verified with adequate comparison studies. Lastly, the derived hybrid models are explored further to by solving different numerical examples for the combined structural parameters (length to width ratio, length to thickness ratio and orthotropicity on frequency and layer-sequence) and the implicit behavior discuss in details.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.9
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• pp.614-619
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• 2010

Recently, a problem related to the thermal management in portable electronic and telecommunication devices is becoming issued. That is due to the trend of a slimness of the devices, so it is not easy to find the optimal thermal management solution for the devices. From now on, a pressed circular type cooling device has been mainly used, however the cooling device with thin thickness is becoming needed by the inner space constraint of the applications. In the present study, the silicon flat plate type cooling device with the separated vapor and liquid flow path was designed and fabricated. The normal isothermal characteristics created by vapor-liquid phase change was confirmed through the experimental study. The cooling device with 70 mm of total length showed 6.8 W of the heat transfer rate within the range of $4{\sim}5^{\circ}C/W$ of thermal resistance. In the future, it will be possible to develop the commercialized cooling device by revising the fabrication process and enhancing the thermal performance of the silicon and glass cooling device.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.2
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• pp.196-202
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• 2012

In this study, numerical analysis of viscous flows is carried out based on the unstructured grid. There exist some difficulties in expressing and computing numerical derivatives on the unstructured grid due to lack of the structured characteristics. The general computer algorithms are developed to perform numerical derivatives easily and extended to be applicable to various geometries composed of hybrid meshes. And the optimal method of strongly implicit procedure is newly contrived to accelerate the rate of convergence in solving the pressure Poisson equation. To verify numerical schemes, the driven cavity problems of 2 and 3 dimension are simulated. The numerical results are compared with others and our numerical schemes are shown to be valid.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.5 no.2
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• pp.25-37
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• 2001

We consider the finite element method applied to nonlinear Sobolev equation with smooth data and demonstrate for arbitrary order ($k{\geq}2$) finite element spaces the optimal rate of convergence in $L_{\infty}\;W^{1,{\infty}}({\Omega})$ and $L_{\infty}(L_{\infty}({\Omega}))$ (quasi-optimal for k = 1). In other words, the nonlinear Sobolev equation can be approximated equally well as its linear counterpart. Furthermore, we also obtain superconvergence results in $L_{\infty}(W^{1,{\infty}}({\Omega}))$ for the difference between the approximate solution and the generalized elliptic projection of the exact solution.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.203-209
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• 2019

The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.43-52
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• 2022

Design of a controller for a high-precision servo control system has been a popular topic while finding optimal parameters for multiple controllers is still a challenging subject. In this paper, we propose a practical scheme to optimize multi-parameters for the robust servo controller design by introducing a new cost function and optimization scheme. The proposed design method provides a simple and practical tool for the systematic servo design to reduce the control error with guaranteeing robust stability of the overall system. The reduction of the position error by 24% along with a faster convergence rate is demonstrated using a typical hard disk drive servo controller with 41 parameters.