• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10A
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• pp.1147-1158
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• 2004

In this paper we design STBC-OFDM systems by applying several STBC schemes to the OFEM system and show their comparative analysis results obtained through computer simulations under Rayleigh fading environments, considering the effect of channel estimation error. We first consider the space-time coding algorithms of major STBC schemes, together with their demodulation algorithms. We then select the OFDMparameters considering mobile environments and design the STBC-OFDM systems by choosing one of digital modulation schemes such as QPSK, BPSK, 16QAM, 64QAM, and 256QAM according to the transmission rate, and describe the block diagrams of the demodulator and channel estimator. We finally compare and analyze the BER performances of the STBC-OFDM systems according to the transmission rate and the number of receive antennas.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.511-515
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• 2004

In the post-genomic era, researches on proteins as well as genes have been increasingly required. Particularly, work on protein-protein interaction and protein network construction have been recently establishing. Most biologists publish their research results through papers or other media. However, biologists do not use the information effectively, since the published research results are very large. As the growth of internet, it becomes easy to access very large research results. It is significantly important to extract information with a biological meaning from varisous media. Therefore, in this research, we efficiently extract protein-protein interaction information from many open papers or other media and construct the database of the extracted information. We build a protein network from the established database and then design and implement various pathway analysis algorithms which find biological meaning from the protein network.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.884-893
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• 2019

Spent fuel racks are steel structures used in the storage of the spent fuel removed from the nuclear power reactor. Rack units are submerged in the depths of the spent fuel pool to keep the fuel cool. Their free-standing design isolates their bases from the pool floor reducing structural stresses in case of seismic event. However, these singular features complicate their seismic analysis which involves a transient dynamic response with geometrical nonlinearities and fluid-structure interactions. An accurate estimation of the response is essential to achieve a safe pool layout and a reliable structural design. An analysis methodology based on the hydrodynamic mass concept and implicit integration algorithms was developed ad-hoc, but some dispersion of results still remains. In order to validate the analysis methodology, vibration tests are carried out on a reduced scale mock-up of a 2-rack system. The two rack mockups are submerged in free-standing conditions inside a rigid pool tank loaded with fake fuel assemblies and subjected to accelerations on a unidirectional shaking table. This article compares the experimental data with the numerical outputs of a finite element model built in ANSYS Mechanical. The in-phase motion of both units is highlighted and the water coupling effect is detailed. Results show a good agreement validating the methodology.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.677-696
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• 2017

Purpose: This study suggests that Six Sigma MBB should be used as a key talent to lead the fourth industrial revolution era by training them with big data processing capability. Methods: Through the analysis between articles on the fourth industrial revolution and Six Sigma related papers, common competencies of data scientists and Six Sigma MBBs were identified and the big data analysis capabilities needed for Six Sigma MBB were derived. Then, training was conducted to improve the big data analysis capabilities so that Six Sigma MBB is able to design algorithms required in the fourth industrial revolution era. Results: Six Sigma MBBs, equipped with the knowledge in field site improvement and basic statistics, were provided with 40 hours of big data analysis training and then were made to design a big data algorithm. Positive results were obtained after applying a AI algorithm which could forecast process defects in a field site. Conclusion: Six Sigma MBB equipped with big data capability will make the best talent for the fourth industrial revolution era. A Six Sigma MBB has an excellent capability for improving field sites. Utilizing the competencies of MBB can be a key to success in the fourth industrial revolution. We hope that the results of this study will be shared with many companies and many more improved case studies will arise in the future as a result of this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.416-424
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• 2017

In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.

• Smart Structures and Systems
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• v.15 no.3
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• pp.847-862
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• 2015

Typical base isolated buildings are designed so that the superstructure remains elastic in design-level earthquakes, though the isolation layer is often quite nonlinear using, e.g., hysteretic elements such as lead-rubber bearings and friction pendulum bearings. Similarly, other well-performing structural control systems keep the structure within the linear range except during the most extreme of excitations. Design optimization of these isolators or other structural control systems requires computationally-expensive response simulations of the (mostly or fully) linear structural system with the nonlinear structural control devices. Standard nonlinear structural analysis algorithms ignore the localized nature of these nonlinearities when computing responses. This paper proposes an approach for the computationally-efficient optimal design of passive isolators by extending a methodology previously developed by the authors for accelerating the response calculation of mostly linear systems with local features (linear or nonlinear, deterministic or random). The methodology is explained and applied to a numerical example of a base isolated building with a hysteretic isolation layer. The computational efficiency of the proposed approach is shown to be significant for this simple problem, and is expected to be even more dramatic for more complex systems.

• Steel and Composite Structures
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• v.15 no.1
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• pp.15-39
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• 2013

The sensitivities of a structural response due to variation of its design parameters are prerequisite in the majority of the algorithms used for fundamental problems in engineering as system uncertainties, identification and probabilistic assessments etc. The paper presents the concept of probabilistic sensitivity of suspension bridges with respect to near-fault ground motion. In near field earthquake ground motions, large amplitude spectral accelerations can occur at long periods where many suspension bridges have significant structural response modes. Two different types of suspension bridges, which are Bosporus and Humber bridges, are selected to investigate the near-fault ground motion effects on suspension bridges random response sensitivity analysis. The modulus of elasticity is selected as random design variable. Strong ground motion records of Kocaeli, Northridge and Erzincan earthquakes are selected for the analyses. The stochastic sensitivity displacements and internal forces are determined by using the stochastic sensitivity finite element method and Monte Carlo simulation method. The stochastic sensitivity displacements and responses obtained from the two different suspension bridges subjected to these near-fault strong-ground motions are compared with each other. It is seen from the results that near-fault ground motions have different impacts stochastic sensitivity responses of suspension bridges. The stochastic sensitivity information provides a deeper insight into the structural design and it can be used as a basis for decision-making.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.52-60
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• 2000

In order to design a reliable machine component efficiently, it is necessary to set up the process of durability analysis using computer simulation technique. In this paper, two methods for dynamic stress calculation, which are basis of durability analysis, are reviewed. Then, a user-oriented dynamic stress analysis program is developed from these two algorithms together with a general-purpose flexible body dynamic analysis and structural analysis programs. Finally, a slider-crank mechanism which has a flexible connecting-rod is chosen to show the special characteristics of these two dynamic stress calculation methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.