• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.581-589
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• 1998

An algorithm is proposed for the $L_1$-estimation with linear equality and inequality constraints in linear regression model. The algorithm employs a linear scaling transformation to obtain the optimal solution of linear programming type problem. And a special scheme is used to maintain the feasibility of the updated solution at each iteration. The convergence of the proposed algorithm is proved. In addition, the updating and orthogonal decomposition techniques are employed to improve the computational efficiency and numerical stability.

• Structural Engineering and Mechanics
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• v.8 no.6
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• pp.547-560
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• 1999

In this paper an asymptotic iteration method is adopted to analyze non-linear free vibration of reticulated circular plates composed of beam members placed in two orthogonal directions. For the resulting linear ordinary differential equations in the process of iteration, the power series with rapid convergence has been applied to obtain an analytical solution for non-linear characteristic relation between the amplitude and frequency of the structure. Numerical examples are given, and the phenomena indicating hardening of such structures have been presented for the (immovable or movable) simply-supported and clamped circular plates.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.1-9
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• 2021

OpenGL compute shader is a shader stage that operate differently from other shader stage and it can be used for the calculating purpose of any data in parallel. This paper proposes a GPU-based parallel algorithm for computing sparse linear systems through conjugate gradient using an iterative method, which perform calculation on OpenGL compute shader. Basically, this sparse linear solver is used to solve large linear systems such as symmetric positive definite matrix. Four well-known matrix formats (Dense, COO, ELL and CSR) have been used for matrix storage. The performance comparison from our experimental tests using eight sparse matrices shows that GPU-based linear solving system much faster than CPU-based linear solving system with the best average computing time 0.64ms in GPU-based and 15.37ms in CPU-based.

• Journal of Ocean Engineering and Technology
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• v.30 no.5
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• pp.405-414
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• 2016

This paper presents an internal oil leakage detection problem for a hydraulic single-rod cylinder. We derive the dynamics of the hydraulic cylinder as a state space model, and then design a T—S fuzzy model-based fault detection observer. We adopt an H_∞ observer design scheme so that the observer is robust against disturbance and relatively sensitive to the leakage fault. Sufficient design conditions are derived in the form of linear matrix inequalities. A numerical example is provided to verify the proposed techniques.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• Communications of the Korean Mathematical Society
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• v.15 no.1
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• pp.143-153
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• 2000

In [8], Franca and Stenberg developed several Galerkin least squares methods for the solution of the problem of linear elasticity. That work concerned itself only with the error estimates of the method. It did not address the related problem of finding effective methods for the solution of the associated linear systems. In this work, we propose the block diagonal preconditioners. The preconditioned conjugate residual method is robust in that the convergence is uniform as the parameter, v, goes to $\sfrac{1}{2}$. Computational experiments are included.

• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.587-600
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• 2021

Some problems in engineering and science can be equivalently transformed into solving multi-linear systems. In this paper, we propose two preconditioned AOR iteration methods to solve multi-linear systems with -tensor. Based on these methods, the general conditions of preconditioners are given. We give the convergence theorem and comparison theorem of the two methods. The results of numerical examples show that methods we propose are more effective.

• Communications of the Korean Mathematical Society
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• v.34 no.2
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• pp.657-670
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• 2019

In this paper, we consider the preconditioned iterative methods for solving linear complementarity problem associated with an M-matrix. Based on the generalized Gunawardena's preconditioner, two preconditioned SSOR methods for solving the linear complementarity problem are proposed. The convergence of the proposed methods are analyzed, and the comparison results are derived. The comparison results showed that preconditioned SSOR methods accelerate the convergent rate of the original SSOR method. Numerical examples are used to illustrate the theoretical results.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.3
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• pp.10-17
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• 1995

this thesis proposes a new echo canceller that can be used in a fulll-duplex digital subscriber loopmodem. The modem suffers from nonlinear distortion such as transmitted pulse asymmetry, saturation in transformers, and nonlinearity of data converters. The proposed nonlinear echo canceller can compensate the nolinear distortion by using a nonlinear digital filter based on canonical pieceewise-linear (CPWL) function. Numerical results based on computer simulation are given in this paper. It is shown that the convergence characteristics depend on the initial values of weights of linear filters with absoluters and that the nonlinearity in digital-to-analog(D/A) converter can be compensated by a relatively small number linear filters with absoluters. It is also shown that the proposed algorithm has a faster convergence rate in comparison with Voterra algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.2
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• pp.141-147
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• 2003

In this paper, the vibration characteristics of a linear stepping motor(LSM) are analyzed using the finite element method(FEM : Flux2D) and ACSL. A magnetic equivalent circuit is based on the structure of the LSM, and then the electric equivalent circuit of the LSM is derived by solving equations for the magnetic equivalent circuit. A normal force is calculated using FEM. And the vibration characteristics of the LSM are simulated by the ACSL with the voltage equations, the thrust equations, the normal force equations and the kinetic equations, and are measured by LASER experimental system.