• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.247-250
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• 2006

Integrating a GPS based vehicle navigation system and the latest optimal algorithms, this research aims to develop a real-time decision support platform for concrete plant to provide the optimal solutions for ready mixed concrete delivery. The platform includes fleet tracking system, simulation and optimization tools, and visual interface which is useful to monitor delivery progress, to obtain crucial historical and real-time data for simulation, and to improve the efficiency of the plant operation. This paper presents configuration of the system and performance evaluation based on operational data.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.171-179
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• 2011

In this paper, we deal with the vehicle routing problem that could establish operational plan of military engineer for survivability support of artillery position construction. We propose VRPTW(vehicle routing problem with time-window) model of special form that considered service level to reflect the characteristics of military operations rather than the logic of economic efficiencies in the objective function. Furthermore we suggest modified particle swarm optimization algorithm for service based vehicle routing problem solution that can be possible to search in complicated and uncertain area and control relation softly between global and local search.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.166-172
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• 2020

Video streaming application such as YouTube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate scheme to select a proper video representation that the network can support. The download behavior of video streaming client player is governed by several parameters such as maximum buffer size. Especially, the size of the maximum playback buffer in the client player can greatly affect the user experience. To tackle this problem, in this paper, we propose the maximum buffer size optimization according to available network bandwidth and buffer status. Our simulation study shows that our proposed buffer size optimization scheme successfully mitigates playback stalls while preserving the similar quality of streaming video compared to existing ABR schemes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.5
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• pp.538-544
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• 2012

In this paper, design optimization of C-frame of a precision drilling and autorivet machine has been performed. The machine, Autoriveter has been developed by Korea Aerospace Industry (KAI), For current autoriveter, it is hard to achieve high efficiency because of heavy weight of the machine. In this paper, we suggest new structure of the current C-frame, a part of autoriveter, by optimization. The result of the study can give much profit for mass-production of the machine.

• Structural Engineering and Mechanics
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• v.31 no.4
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• pp.393-405
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• 2009

The Purpose of this paper is to show the applicability of a methodology, developed by the authors, with which to perform the mechanical optimization of space truss structures strongly restricted. This methodology use a parameter call "Volumetric Displacement", as the Objective Function of the optimization process. This parameter considers altogether the structure weight and deformation whose effects are opposed. The Finite Element Method is employed to calculate the stress/strain state and the natural frequency of the structure through a structural linear static and natural frequency analysis. In order to show the potentially of this simple methodology, its application on a large diameter telescope structure (10 m) considering the strongly restriction that became of its use, is presented. This methodology, applied in previous works on continuous structures, such as shell roof and fluid storage vessels, is applied in this case to a space truss structure, with the purpose of generalize its applicability to different structural topology. This technique could be useful in the morphology design of deployable and retractable roof structures, whose use has extensively spread in the last years.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.235-240
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• 1998

A procedure for the optimal design of high quality rectifiers is introduced in this paper. The procedure is capable of producing different optimal designs for the same rectifier based on the objective performance required from that rectifier. A FORTRAN-based computer system designed to solve large-scale optimization problems was used in this work to obtain the optimal designs. The optimization program uses Wolfe algorithm in conjunction with a quasi-Newton algorithm as well as a projected augmented Lagrangian algorithm to solve the highly nonlinear optimization problem. The paper also introduces a detailed analysis and an application of the procedure on a boost-type zero-current switch (ZCS) single-switch three-phase rectifier introduced recently in the literature. The obtained results are compared with popular simulation packages (i. e. PSPICE and SIMCAD) to support the validity of the proposed concept.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4835-4855
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• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.