• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.434-445
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• 2020

In this study, the minimization of harbor oscillation using permeable breakwater was applied to the actual harbor and investigated an effect of minimization by computer simulation in order to take into account the water quality problems and measures of harbor oscillation by infragravity waves at the same time. The study site is Mukho harbor located at East coast of Korea that harbor oscillation has been occurred frequently. The infragravity waves obtained by analyzing the observed field data for five years focused on the distribution between wave periods of 40 s and 70 s and wave heights in less than 0.1 m was 94% of analyzing data. The target wave periods was 68.0 s. The most effective method of minimization of harbor oscillation by infragravity waves was to install a detached permeable breakwater with transmission coefficient of 0.3 on the outside harbor and replace some area of the vertical wall in the harbor with wave energy dissipating structure to achieve a reflectivity of 0.9 or less. The amplitude reduction rate of this method shown in 27.4%. And the effect of the difference in transmission coefficient of permeable breakwater on the reduction rate of the amplitude was not significant.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.258-260
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• 2007

MAS based market simulator has attracted the attentions of people who are interested in using or developing electricity market simulator. MAS based approach makes it possible to model each market participant's strategic behaviors. Traditional market simulators have used optimization formulation to model market operation, which has been used since vertically integrated system. Optimization mainly uses cost minimization or welfare maximization of entire system. Therefore it is somehow difficult to model the independently strategic behaviors of market participants. MAS is one of AI technology based on distributed intelligence which makes it possible to model independently acting entities in competitive market. This paper proposes the method to model strategic participants in electricity market based on MAS.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.543-550
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• 1998

From the power plant in a steel plant, environment pollutants such as $SO_x$, $NO_x$, CO and $CO_2$ are emitted by combustion reactions of the fuels which are by-product gases, oil and liquefied natural gas(LNG). To reduce the amounts of the pollutants, it is important to build a predictive model for the emission of the pollutants. In this paper, model that predict the amounts of generated pollutants for the used fuel is developed by using Gibbs free energy minimization method[1] with the temperature correction technique. For some data set, the calculation results from this model are compared with the real emission amounts of $SO_x$, $NO_x$, and the result of the calculation by both ASPEN PLUS which is a commercial simulation software. This model shows good results and can be applied to other power plants.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.3
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• pp.144-154
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• 1994

An efficient tidal model, TIDE which is an iterative type, nonlinear finite element model has developed for the analysis of the tidal movement in the coastal area which is characterized by irregular boundaries and bottom topography. Traditional time domain finite element models have been in difficulties with requirement for high eddy viscosity coefficients and small time steps to insure numerical instability. These problems are overcome by operating in the frequency domain with an elaborate grid system by combining the triangular and quadrilateral shape grids. Furthermore, in order to handle non-linearity which will be more significant in the shallow region, an iterative scheme with least square error minimization algorithm has been implemented in the model. The results of TIDE model are agreed with the analytical solutions in a rectangular channel under the condition of tidal waves entering the channel closed at one end.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.47-50
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• 2006

An active contour model has been used to detect the edges in a still image. In order to apply the active contour model to edge detection, the energy function which consists of internal, external and image energies should be defined. After defining the energy function, the edge of an object is detected through minimization of the value of the energy function. In this paper, the modified internal energy function is proposed to improve the convergence of the energy function when the active contour model is applied to the tracking of deformable objects using the greedy algorithm. In order to show the performance of the proposed energy function, experiments were carried out for the still and animated images.

• Journal of Korean Society of Transportation
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• v.5 no.1
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• pp.47-58
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• 1987

A commodity distribution problem with intertemporal storage facilities and dynamic transportation networks is proposed. mathematical integer programming methods and multiobjective programming techniques are used in the model formulation. Dynamic characteristics of commodity distribution problems are taken into account in the model formulation. storage facility location problems and transportation link addition problems are incorporated into the intertemporal multicommodity distribution problem. The model is capable of generating the most efficient and rational commodity distribution system. Therefore it can be utilized to provided the most effective investment plan for the transportation infrastructure development as well as to evaluate the existing commodity distribution system. The model determines simultaneously the most efficient locations, sizes, and activity levels of storage facilities as well as new highway links. It is extended to multiobjective planning situations for the purpose of generating alternative investment plans in accordance to planning situations. sine the investment in transportation network improvement yields w\several external benefits for a regional economy, the induced benefit maximization objective is incorporated into the cost minimization objective. The multiobjective model generates explicitly the trade-off between cost savings and induced benefits of the investment in transportation network improvement.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.4
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• pp.83-100
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• 2008

We propose a new aggressive formulation of cross-efficiency in Data Envelopment Analysis(DEA). In the traditional aggressive formulation, the efficiency score of a test DMU is maximized as the first goal while an average of efficiency scores of peer DMUs is minimized as the second goal. The proposed model replaces the second goal with the minimization of the best efficiency score of peer DMUs. We showed the model is a quasi-convex optimization problem, and for a solution method we developed a bisection method whose computational complexity is polynomial-time. We tested the model on 200 randomly generated DEA problems, and compared it with the traditional model in terms of various criteria. The experimental results confirmed the effectiveness and usefulness of the proposed model.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.3
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• pp.156-184
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• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.165-176
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• 2011

In this paper, we develop the cost minimization model to select two incentives and total order quantity with consideration of remanufacture company's return incentive. Return rate is sensitive to the incentive that the manufacture company offers. Using a EOQ(Economic Order Quantity) model of a cost minimization, we show concavities of the model about two incentives and total order quantity respectively. According to the proposed algorithm using the concavities, we find out the optimized incentive prices and total order quantity. Through numerical study, we examine sensitive analysis of the incentive price and order quantity for each parameter when the return rate is sensitive to incentive. Company lessens incentive to reduce total price. However, this makes the total price increase due to a diminution of return quantity. We expect that domestic or overseas remanufacture businesses are able to decide optimal incentive and total order quantity by this research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.