• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.133-148
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• 2006

This paper proposes a smoothing method for symmetric conic linear programming (SCLP). We first characterize the central path conditions for SCLP problems with the help of Chen-Harker-Kanzow-Smale smoothing function. A smoothing-type algorithm is constructed based on this characterization and the global convergence and locally quadratic convergence for the proposed algorithm are demonstrated.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.80-86
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• 2002

A neural network structure which is able to perform the operations of analog addition and linear equation is proposed. The network employs Hopfkeld's model of a neuron with the connection elements specified on the basis of an analysis of the energy function. The analog addition network and linear equation network are designed by using Hopfield's A/D converter and linear programming respectively. Simulation using Pspice has shown convergence predominently to the correct global minima.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.165-177
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• 2005

In this paper, an adaptive trust region method based on the conic model for unconstrained optimization problems is proposed and analyzed. We establish the global and super linear convergence results of the method. Numerical tests are reported that confirm the efficiency of the new method.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1738-1739
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• 2011

In this paper, the range difference based the moving target tracking problem using multiple UAVs is solved within the new framework of linear robust state estimation. To do this, the relative kinematics is modeled as an uncertain linear system containing stochastic parametric uncertainties in its measurement matrix. Applying the non-conservative robust Kalman filter for the uncertain system, a quasi-optimal linear target tracking filter is designed. For its recursive linear filter structure, the proposed method can ensure the fast convergence and reliable target tracking performance. Moreover, it is suitable for real-time applications using multiple UAVs.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1559-1566
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• 2015

The brushless DC motor (BLDCM) has many advantages. As a result, it is widely used in electric vehicle (EV) drive systems. To improve the reliability of the motor control system, a position sensorless control strategy based on a sliding mode observer (SMO) is proposed. The global fast terminal sliding mode observer (GFTSMO) is proposed to enhance the control performance of the SMO control system. The advantages of the linear sliding mode and the nonsingular terminal sliding mode (NTSM) are combined in the control strategy. The convergence speed of the system state is enhanced. The motor commutation point is obtained with the observation of the back EMF, and the instantaneous torque value of the motor is calculated. Therefore, the position sensorless control of the BLDCM is realized. Experimental results show that the proposed control strategy can improve the convergence speed, dynamic characteristics and robustness of the system.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.159-159
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• 2022

Soil represents a substantial component within the global carbon cycle and small changes in the SOC stock may result in large changes of atmospheric CO₂ particularly over tens to hundreds of years. In this study, we aim to (i) evaluate the SOC stock in the topsoil 0 - 15 cm from soil physical and chemical characteristics and (ii) find the correlation of SOC and soil organic matter (SOM) for national-scale in South Korea. First of all, based on the characteristics of the soil to calculate the soil hydraulic properties, SOC stock is the SOC mass per unit area for a given depth. It depends on bulk density (BD-g/cm³), SOC content (%), the depth of topsoil (cm), and gravel content (%). Due to insufficient data on BD observation, we establish a correlation between BD and SOC content, sand content, clay content parameter. Next, we present linear and non-linear regression models of BD and the interrelationship between SOC and SOM using a linear regression model and determine the conversion factor for them, comparing with Van Bemmelen 1890's factor value for the country scale. The results obtained, helps managers come up with suitable solutions to conserve land resources.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.251-256
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• 2023

International diesel prices play a crucial role in various sectors such as industry, transportation, and energy production, exerting a significant impact on the global economy and international trade. In particular, an increase in international diesel prices can burden consumers and potentially lead to inflation. However, previous studies have primarily focused on gasoline. Therefore, this study aims to propose an international diesel price prediction model. To achieve this goal, we utilize various global economic indicators and train a linear regression model, which is one of the machine learning methodologies. This model clearly identifies the relationship between global economic indicators and international diesel prices while providing highly accurate predictions. It is expected to aid in understanding overall economic trends including market changes.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1395-1407
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• 2011

When a Sequential Quadratic Programming (SQP) method is used to solve the nonlinear programming problems, one of the main difficulties is that the Quadratic Programming (QP) subproblem may be incompatible. In this paper, an SQP algorithm is given by modifying the traditional QP subproblem and applying a class of $l_{\infty}$ penalty function whose penalty parameters can be adjusted automatically. The new QP subproblem is compatible. Under the extended Mangasarian-Fromovitz constraint qualification condition and the boundedness of the iterates, the algorithm is showed to be globally convergent to a KKT point of the non-linear programming problem.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.211-225
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• 2014

A new class of smoothing functions is introduced in this paper, which includes some important smoothing complementarity functions as its special cases. Based on this new smoothing function, we proposed a smoothing Newton method. Our algorithm needs only to solve one linear system of equations. Without requiring the nonemptyness and boundedness of the solution set, the proposed algorithm is proved to be globally convergent. Numerical results indicate that the smoothing Newton method based on the new proposed class of smoothing functions with ${\theta}{\in}(0,1)$ seems to have better numerical performance than those based on some other important smoothing functions, which also demonstrate that our algorithm is promising.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.653-668
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• 2011

This paper presents a new hybrid method for solving nonlinear complementarity problems with $P_0$-functions. It can be regarded as a combination of smoothing trust region method with ODE-based method and line search technique. A feature of the proposed method is that at each iteration, a linear system is only solved once to obtain a trial step, thus avoiding solving a trust region subproblem. Another is that when a trial step is not accepted, the method does not resolve the linear system but generates an iterative point whose step-length is defined by a line search. Under some conditions, the method is proven to be globally and superlinearly convergent. Preliminary numerical results indicate that the proposed method is promising.