• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.303-310
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• 2005

The systems with probe and SIL(Solid Immersion Lens) mechanisms have been researched as the technology to perform NFR(Near Field Recording). Most of them use the flying head mechanism to accomplish high recording density and fast data transfer rate. In this paper, ABS shape of flying head was optimized with the object of securing the maximum compliance ability of OFH. We suggest low different optimization processes to predict the static flying characteristics for the OFH. Two different approximation methods, regression analysis and back propagation neural network were used. And we compared the result of directly connected(between CAE and optimizer) method and two approximated optimization results. Design Optimization Tool(DOT) and ${\mu}GA$ were used as the optimizers.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.466-473
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• 2000

Optimality Criteria algorithm based on the derivation of reciprocal approximations has been applied to structural optimization of large-scale structures. However, required computational cost for the serial analysis algorithm of large-scale structures consisting of a large number of degrees of freedom and members is too high to be adopted in the solution process of O.C. algorithm Thus, parallel version of O.C. algorithm on the network of personal computers is presented in this Paper. Parallelism in O.C. algorithm may be classified into two regions such as analysis and optimizer part As the first step of development of parallel algorithm, parallel structural analysis algorithm is developed and used in O.C. algorithm The algorithm is applied to optimal design of a 54-story plane frame structure

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.103-110
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• 2004

Recently, performance based seismic design (PBSD) methods in numerous forms have been suggested and widely studied as a new concept of seismic design. The PBDSs are far from being practical due to complexity of algorithms resided in the design philosophy In this paper, optimal seismic design method based on displacement coefficient method (DCM) described in FEMA 273 is developed. As an optimizer simple genetic algorithms are used for implementations. In the optimization problem formulated in this paper, strength design criteria, stiffness design criteria, and nonlinear response criteria specified in DCM are included in design constraints. The optimal performance based design(OPBD) method is applied to seismic design of a 9-story two-dimensional steel frame structures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.636-643
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• 2005

Recently, performance based seismic design (PBSD) methods have been suggested in numerous forms and widely studied as a new concept of seismic design. The PBDSs are far from being practical method due to complexity of algorithms resided in the design philosophy. In this paper, optimal seismic design method based on displacement coefficient method (DCM) described in FEMA 273 is developed. As an optimizer simple genetic algorithms are used for implementations. In the optimization problem formulated in this Paper, strength design criteria stiffness design criteria, and nonlinear response criteria specified in DCM are included in design constraints. The optimal performance based design(OPBD) method is applied to seismic design of a 3-story two-dimensional steel frame structures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.458-465
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• 2000

In the field of structural optimization simulated annealing (SA) algorithm has widely been adopted as an optimizer with the positive features of SA such as simplicity of the algorithm and possibility of finding global solution However, annealing process of SA algorithm based on random generator with the zeroth order structural information requires a large of number of iterations highly depending on cooling schedules and stopping criteria. In this paper, MSA algorithm is presented in the form of two phase annealing process with the effective cooling schedule and stopping criteria. With the application to optimal seismic design of steel structures, the performance of the proposed MSA algorithm has been demonstrated with respect to stability and global convergence of the algorithm

• Journal of IKEEE
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• v.22 no.1
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• pp.14-20
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• 2018

The purpose of training a convolutional neural network (CNN) is to obtain weight factors that give high classification accuracies. The initial values of hyper-parameters affect the training results, and it is important to train a CNN with a suitable hyper-parameter set of a learning rate, a batch size, the initialization of weight factors, and an optimizer. We investigate the effects of a single hyper-parameter while others are fixed in order to obtain a hyper-parameter set that gives higher classification accuracies and requires shorter training time using a proposed VGG-like CNN for training since the VGG is widely used. The CNN is trained for four datasets of CIFAR10, CIFAR100, GTSRB and DSDL-DB. The effects of the normalization and the data transformation for datasets are also investigated, and a training scheme using merged datasets is proposed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.1
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• pp.95-101
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• 2006

Minimum weight design of laminated composite panel under combined loading was studied using linear and nonlinear deformation theories and by closed-form analysis and finite difference energy methods. Various buckling load factors are obatined for laminated composite panels with rectangular type longitudinal stiffeners and various longitudinal length to radius ratios, which are made from Carbon/Epoxy USNl25 prepreg and are simply-supported on four edges under combined loading, and then for them, minimum weight design analyses are carried out by the nonlinear search optimizer, ADS. This minimum weight design analyses are constructed with various process such as the simple design process, test simulation process and sensitivity analysis. Subseguently, the buckling mode shapes are obtained by buckling and minimum weight analyses.

• Journal of the Korean Society of Marine Environment & Safety
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• v.8 no.1
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• pp.127-137
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• 2002

This paper deals with developing a Web-based Solver NRO(Network Reliability Optimizer) for solving three classes of reliability redundancy optimization problems which are generated in series systems. parallel systems and complex systems. Inputs of NRO consisted in four parts. that is, user authentication. system selection. input data and confirmation. After processing of inputs through internet, NRO provides conveniently the optimal solutions for the given problems on the Web-site. To alleviate the risks of being trapped in a local optimum, HH(Hybrid-Heuristic) algorithm is incorporated in NRO for solving the given three classes of problems, and moderately combined GA(Genetic Algorithm) with the modified SA(Simulated Annealing) algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.614-622
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• 2010

The goal of this paper is to develope the control program based on model which can be applied to 1000MW class coal fired thermal power plant. 1000MW class power plant has the higher efficiency and lower cost because the steam conditions of the ultra super-critical process are higher than them of the previous power plants in temperature and pressure. The program includes the state variable controls which have the desired characteristics for the higher temperature and pressure. The program had been developed successfully using advanced process control. The simulation results using the new control program showed the better performance and safer control than them of the previous control program and we could verify the application possibility of the new program for the actual power plant through the load test, comparison, analysis and tuning.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.93-100
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• 2008

Quality function deployment (QFD) provides a specific approach for ensuring quality throughout each stage of the product development and production process. Since the focus of QFD is placed on the early stage of product development, the uncertainty in the input information of QFD is inevitable. If the uncertainty is neglected, the QFD analysis results are likely to be misleading. It is necessary to equip practitioners with a new QFD methodology that can model, analyze, and dampen the effects of the uncertainty and variability in a systematic manner. Robust QFD is an extended version of QFD methodology, which is robust to the uncertainty of the input information and the resulting variability of the QFD output. This paper discusses recent research issues in Robust QFD. The major issues are related with the determination of overall priority, robustness evaluation, robust prioritization, and web-based Robust QFD optimizer. Our recent research results on the issues are presented, and some of future research topics are suggested.