• Journal of Communications and Networks
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• v.14 no.5
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• pp.554-565
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• 2012

In this paper, we study a cross-layer and end-to-end optimization problem for the integrated wireless and wireline network that consists of one wireline core network and multiple wireless access networks. We consider joint end-to-end flow control/distribution at the transport and network layers and opportunistic scheduling at the data link and physical layers. We formulate a single stochastic optimization problem and solve it by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way, vertically among communication layers and horizontally among all entities in the network, clearly showing what should be done at each layer and each entity and what parameters should be exchanged between layers and between entities. Numerical results show that our cross-layer and end-to-end optimization approach provides more efficient resource allocation than the conventional layered and separated optimization approach.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.6
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• pp.579-585
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• 2009

The multifrequency eddy current testing(ECT) have been proposed various frequency mixing algorithms. In this study, we compare these approaches to frequency mixing of ECT signals from steam generator tubes; time-domain optimization, discrete cosine transform-domain optimization. Specifically, in this study, two different frequency mixing algorithms, a time-domain optimization method and a discrete cosine transform(DCT) optimization method, are investigated using the experimental signals captured from the ASME standard tube. The DCT domain optimization method is computationally fast but produces larger amount of residue.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.822-827
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• 2005

Recently engineering systems problems become quite large and complicated. For those problems, design requirements are fairly complex. It is not easy to design such systems by considering only one discipline. Therefore, we need a design methodology that can consider various disciplines. Multidisciplinary Design Optimization (MDO) is an emerging optimization method to include multiple disciplines. So far, about seven MDO methodologies have been proposed for MDO. They are Multidisciplinary Feasible (MDF), Individual Feasible (IDF), All-at-Once (AAO), Concurrent Subspace Optimization (CSSO), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS) and Multidisciplinary Optimization Based on Independent Subspaces (MDOIS). In this research, the performances of the methods are evaluated and compared. Practical engineering problems may not be appropriate for fairness. Therefore, mathematical problems are developed for the comparison. Conditions for fair comparison are defined and the mathematical problems are defined based on the conditions. All the methods are coded and the performances of the methods are compared qualitatively as well as quantitatively.

• Structural Engineering and Mechanics
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• v.62 no.3
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• pp.259-272
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• 2017

In this study, recently developed swarm intelligence algorithm called Social Spider Optimization (SSO) approach and its enhanced version of SSO algorithm with spider jump techniques is used to develop a structural optimization technique for steel space structures. The improved version of SSO uses adaptive randomness probability in generating new solutions. The objective function of the design optimization problem is taken as the weight of a steel space structure. Constraints' functions are implemented from American Institute of Steel Construction-Load Resistance factor design (AISC-LRFD) and Ad Hoc Committee report and practice which cover strength, serviceability and geometric requirements. Three steel space structures are optimized using both standard SSO and SSO with spider jump (SSO_SJ) algorithms and the results are compared with those available in the literature in order to investigate the performance of the proposed algorithms.

• Structural Engineering and Mechanics
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• v.58 no.6
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• pp.1109-1126
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• 2016

The present paper focuses on size optimization of double layer barrel vaults considering nonlinear behavior. In order to tackle the optimization problem an improved colliding bodies optimization (ICBO) algorithm is proposed. The important task that should be achieved before optimization of structural systems is to determine the best form having the least cost. In this study, an attempt is done to find the best form then it is optimized considering linear and non-linear behaviors. In the optimization process based on nonlinear behavior, the geometrical and material nonlinearity effects are included. A large-scale double layer barrel vault is presented as the numerical example of this study and the obtained results indicate that the proposed ICBO has better computational performance compared with other algorithms.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.124-129
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• 2013

Electro-Mechanical Batteries have important advantages compared with chemical batteries, especially in Low Earth Orbit satellites applications. High speed, slotless, external rotor, brushless DC machines are proposed and used in these systems as Motor/Generator. A simplified analytic design method is given for this type of machines and, the optimization of machine in order to have maximum efficiency and minimum volume and weight are given in this paper. Particle swarm optimization (PSO) is used as the optimization algorithm and the finite element-based simulations are used to confirm the design and optimization process and show less than 6% error in parametric design.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.130-133
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• 2008

In this paper the trust region method is studied and applied in aerodynamic shape optimization. The trust region method is a gradient-based optimization method, but it is not as popular as other methods in engineering computations. Its theory will be explained for unconstrained optimization problems and a trust region subproblem will be solved with the dogleg method. After verifying the trust region method with analytical test problems, it is applied to aerodynamic shape design optimization and the performance of airfoil is improved successfully.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.155-160
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• 2001

To achieve high precision cutting as well as production capability in the machine tool, it is needed to develop excellent rigidity statically, dynamically and thermally as well. In order to predict the qualitative behavior of a machine tool, simultaneous analysis of mechanics and heat transfer is required. Generally, machine tool designers have solved designing problems based on partial estimation of the specified rigidity. This study clears the inter-relationship between therm, and propose multi-phase optimization of machine tool structure using a genetic algorithm. The multi-phase solution method is consists of a series of mechanical design problem. At this first phase of static design problem, multi-objective optimization for the purpose of minimization of the total weight and static compliance minimization is solved using the Pareto Genetic Algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1189-1195
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• 2011

For side airbag, the pipe type inflators have been wide used while the disk type inflators have been used for front airbag. For helping to prevent injury and death the airbag inflator system should be design with great care. The present study deal with optimizing the design of side airbag inflator by finite element analysis and design of experiment method. An optimization process was integrated to determine the optimum design variable values related to the side airbag inflator. Free shape optimization method has been carried out to find a optimal shape on an side airbag inflator model. Optimization of the air bag inflator was successfully developed using Sharpe optimization was carried out to find a new geometry. The improved results compared to the base design specification were achieved from design of experiment and optimization.

• Advances in Computational Design
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• v.1 no.2
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• pp.175-194
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• 2016

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. Dolphin Echolocation Optimization (DEO) is an optimization method based on dolphin's approach for hunting food and exploration of environment. DEO has already been developed for discrete optimization search space and here it is extended to continuous search space. DEO has simple rules and is adjustable for predetermined computational cost. DEO provides the optimum results and leads to alternative optimality curves suitable for the problem. This algorithm has a few parameters and it is applicable to a wide range of problems like other metaheuristic algorithms. In the present work, the efficiency of this approach is demonstrated using standard benchmark problems.