• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.311-326
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• 2011

In this paper, we adopt discontinuous Galerkin methods with penalty terms namely symmetric interior penalty Galerkin methods, to solve nonlinear viscoelasticity type equations. We construct finite element spaces and define an appropriate projection of u and prove its optimal convergence. We construct extrapolated fully discrete discontinuous Galerkin approximations for the viscoelasticity type equation and prove ${\ell}^{\infty}(L^2)$ optimal error estimates in both spatial direction and temporal direction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.147-159
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• 1991

Stochastic convexity(concvity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through the probabilistic construction based on the sample path approach. A Markov additive process is obtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or for optimal operation schedule of a wide range of stochastic systems. We also clarify the conditions for stochatic monotonicity of the Markov process, which is required for stochatic convexity of the Markov additive process. This result shows that stochastic convexity can be used for the analysis of probabilistic models based on birth and death processes, which have very wide application area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.4
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• pp.231-239
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• 1987

Distribution system is one of large and complicated sytem, consisted of a great number of components. Therefore efficient operation based on precise analysis and computation methods is indispensable accommodating growing loads. This paper describes an optimal operation problem to relieve overload flow in radial distribution systems by using modified block model. The problem is formulated as a network problem of synthesizing the optimal spanning tree in a graph, branch and bound method is used for the optimization. Especially modified block model proposed in this paper is validated more practical than conventional model. These methods can be applied to two types of distribution system problems such as, 1) planning problem to check the capability of relieving overload at normal rating, 2) emergency operation problem to determine switching scheme for minimizing customer loads affected by a fault. Examples of application to these problems are discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.76-88
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• 1991

Stochastic convexity (concavity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through probabilistic construction based on the sample path approach. A Markov additive process is abtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or optimal operation schedule wide range of stochastic systems. We also clarify the conditions for stochastic monotonicity of the Markov process. From the result it is shown that stachstic convexity can be used for the analysis of probabilitic models based on birth and death processes, which have very wide applications area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.E2
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• pp.75-81
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• 2003

Variational data assimilation, which is recently introduced to the air quality modeling, is a promising tool for obtaining optimal estimates of initial conditions and other important parameters such as emission and deposition rates. In this paper. two advanced techniques for variational data assimilation, based on the adjoint and quasi-inverse methods, are tested for a simple air quality problem. The four-dimensional variational assimilation (4D-Var) requires to run an adjoint model to provide the gradient information in an iterative minimization process, whereas the inverse 3D-Var (I3D-Var) seeks for optimal initial conditions directly by running a quasi -inverse model. For a process with small dissipation, I3D-Vu outperforms 4D-Var in both computing time and accuracy. Hybrid application which combines I3D-Var and standard 4D-Var is also suggested for efficient data assimilation in air quality problems.

• Interaction and multiscale mechanics
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• v.1 no.3
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• pp.357-368
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• 2008

The result of damage detection from on-site measurements is commonly polluted by unavoidable measurement noises. It is widely recognized that this side influence could be reduced to some extent if the sensor placement was properly designed. Although many methods have been proposed to find the optimal number and location of mono-type sensors, the optimal layout of multi-type sensors need further investigation, because a network of heterogeneous sensors is commonly used in engineering. In this paper, a new criterion of the optimal placement for different types of sensors is proposed. A corresponding heuristic is developed to search for good results. In addition, Monte Carlo simulation is suggested to design a robust damage detection system which contains certain redundancies. The validity of these methods is illustrated by two bridge examples.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.45-53
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• 2010

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.10-14
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• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• Journal of Microbiology
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• v.37 no.4
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• pp.263-266
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• 1999

In examining bacterial growth on glass surfaces immersed in sea water, we found serious differences between enumeration methods. Therefore, we compared various methods and found sonication and direct count methods were superior to other methods. Since the direct count method was not suitable for long-term investigation, we chose the sonication method and confirmed that sonication periods 8 times for 30 seconds was optimal for the detachment of bacteria from glass surfaces.