• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.117-131
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• 2021

The problem of generalized thermoelasticity of two-temperature for finite piezoelectric rod will be modified by applying three different types of heating applications namely, thermal shock, ramp-type heating and harmonically vary heating. The solutions will be derived with direct approach by the application of Laplace transform and the Caputo-Fabrizio fractional order derivative. The inverse Laplace transforms are numerically evaluated with the help of a method formulated on Fourier series expansion. The results obtained for the conductive temperature, the dynamical temperature, the displacement, the stress and the strain distributions have represented graphically using MATLAB.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.872-881
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• 1996

The concept of homology design has been devised for the application to large telescope structure by S.v.Hoerner. It is defined that the deformation of a structure shall be called homologous, if a given geometrical relation holds, for a given number of structural points, before, during, and after the deformation. Recently, the need of homology design in the structural design has been increase due to the required precision in the structure. Some researchers have utilized the theory on the structural design with finite element method in the late 1980s In the present investigation, a simple method using geometrical equality constraints is suggested to gain homologous deformation. The previous method is improved in that the decomposition of FEM eqation, which is very expensive, is not necessary. The basic formulations of the homology design with the optimization concept are described and several practical examples are solved to verify the usefulness and validity. Especially, a back-up structure of a satellite antenna is designed by the suggested method. The results are compared with those of existing researches.

• Coupled systems mechanics
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• v.11 no.1
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• pp.33-41
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• 2022

Quite often we have a lot of measurement data and would like to find some relation between them. One common task is to see whether some measured data or a curve of known shape fit into the cumulative measured data. The problem can be visualized since data could generally be presented as curves or planes in Cartesian coordinates where each curve could be represented as a vector. In most cases we have measured the cumulative 'curve', we know shapes of other 'curves' and would like to determine unknown coefficients that multiply the known shapes in order to match the measured cumulative 'curve'. This problem could be presented in more complex variants, e.g., a constant could be added, some missing (unknown) data vector could be added to the measured summary vector, and instead of constant factors we could have polynomials, etc. All of them could be solved with slightly extended version of the procedure presented in the sequel. Solution procedure could be devised by reformulating the problem as a measurement problem and applying the generalized inverse of the measurement matrix. Measurement problem often has some errors involved in the measurement data but the least squares method that is comprised in the formulation quite successfully addresses the problem. Numerical examples illustrate the solution procedure.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.11-18
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• 2009

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix. the Levenberg-Marquardt learning must increase or decrease ${\mu}$ and recalculate the inverse matrix of the Jacobian matrix due to these changes of ${\mu}$. Therefore, to have the proper ${\mu}$, we create the principal submatrix of Jacobian matrix and set the ${\mu}$ as the eigenvalues sum of the principal submatrix. which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.269-275
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• 2013

In this paper, a new modeling method of a magnetic levitation(Maglev) system using extreme learning machine(ELM) is proposed. The linearized methods using Taylor Series expansion has been used for modeling of a Maglev system. However, the numerical method has some drawbacks when dealing with the components with high nonlinearity of a Maglev system. To overcome this problem, we propose a new modeling method of the Maglev system with electro magnetic suspension, which is based on ELM with fast learning time than conventional neural networks. In the proposed method, the initial input weights and hidden biases of the method are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose generalized inverse. matrix Experimental results show that the proposed method can achieve better performance for modeling of Maglev system than the previous numerical method.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2060-2069
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• 1997

Information processing using computer in generalized in the office automation. In spite of to be integrate and concise form of document through computer network, signature of document with hand have processed as ever. The security on document flow out severely unjust by reason of increment inverse function of computer. Because of revelation secret of enterprise result from unjust outflow, lots of loss of self-enterprise is occured. In this paper, we used efficiently document using the method, electronic approval system with encryption, for the resolving above problems. Also we persue maintenance of security for the important document and process document signature rapidly. Finally, we design and implementation of electronic approval system that take one's share of function between server and client using to be transformed Vernam's encryption technique in stored document.

• Atmosphere
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• v.26 no.4
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• pp.697-709
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• 2016

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 m-resolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.129-152
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• 2008

This paper considers the solution of the stochastic differential equations (SDEs) with random operator and/or random excitation using the spectral SFEM. The random system parameters (involved in the operator) and the random excitations are modeled as second order stochastic processes defined only by their means and covariance functions. All random fields dealt with in this paper are continuous and do not have known explicit forms dependent on the spatial dimension. This fact makes the usage of the finite element (FE) analysis be difficult. Relying on the spectral properties of the covariance function, the Karhunen-Loeve expansion is used to represent these processes to overcome this difficulty. Then, a spectral approximation for the stochastic response (solution) of the SDE is obtained based on the implementation of the concept of generalized inverse defined by the Neumann expansion. This leads to an explicit expression for the solution process as a multivariate polynomial functional of a set of uncorrelated random variables that enables us to compute the statistical moments of the solution vector. To check the validity of this method, two applications are introduced which are, randomly loaded simply supported reinforced concrete beam and reinforced concrete cantilever beam with random bending rigidity. Finally, a more general application, randomly loaded simply supported reinforced concrete beam with random bending rigidity, is presented to illustrate the method.

• Journal of Korean Association for Spatial Structures
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• v.10 no.2
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• pp.87-94
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• 2010

Form-Finding of tensegrity structures by eigenvalue problem is presented, In ardor to maintain the structures stable, "Form-Finding" should be performed. The types of analytical methods are known to solve this phenomenon: One is to use force density method, and the other is to apply so called, generalized inverse method. In this paper, new form finding methods are presented to obtain the self-equilibrium stress of the tensegrity structures. This method is based on the equilibrium equation of the all of the joint and the governing equation is formulated as eigonvalue problem. In order to verify this approach, numerical example(tensegrity structures) are compared with others calculated by previous methods. The solution by present method is shown identical results. Furthermore, the developed process to find the results is more efficient than previous approaches.