• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.1
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• pp.63-74
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• 1996

Automatic transmission for heavy duty vehicles is a part of the power pack which includes steering and braking systems. This transmission in different from the one for passenger car. Therefore, in order to understand the trend of the important design parameters, maneuverability, acceleration performance and maximum speed, we need to analyze the total performance characteristics of the power transmission systems. In this study, modeling of the automatic transmission in heavy duty vehicle is carried out and the performance analysis method is presented. Results can be used for performance estimation data in the analysis for several combination method which determines the optimal parameters on the basis of penalty functions and weightings. And the estimation method of the important performance parameters such as engine inertia or power loss of engine by experiments is presented.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.7
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• pp.997-1004
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• 1990

In this paper, a design method of a robust adaptive regulator with feedfoward path based on the explicit criterion minimization is proposed. The convergence speed of parameter estimation is improved by using the stochastic Newton minimization method in the criterion minimization algorithm, and sensitivity derivatives are used in the regulator calculation for improving the robustness of the control system. Trh proposed adaptive regulator is applied to the stable minimum-phase and nonminimum-phase system, the results are shown that control performance and disturbance compensation ability of the regulator are improved. And the choosing method of input penalty is proposed.

• JSTS:Journal of Semiconductor Technology and Science
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• v.5 no.3
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• pp.168-172
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• 2005

In this paper, we present a new adaptive biasing scheme for CMOS op-amps. The designed circuit has been used in an Operational Transconductance Amplifier (OTA) with ${\pm}1$ V power supply, and it has improved the positive and negative slew rates from 2.92 V/msec to 1242 V/msec and from 1.56 V/msec to 133 V/msec respectively, while maintaining all the small-signal performance parameter values the same as that without adaptive biasing (as expected), however, there was a marginal decrease of the dynamic range. The most useful features of the proposed circuit are that it uses a very low number of components (thus not creating severe area penalty) and requires only 25 nW of extra stand-by power.

• East Asian mathematical journal
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• v.28 no.3
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• pp.293-303
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• 2012

Sequential quadratic programming (SQP) has been one of the most important methods for solving nonlinear constrained optimization problems. Recently, filter method, proposed by Fletcher and Leyffer, has been extensively applied for its promising numerical results. In this paper, we present and study an active set SQP-filter algorithm for inequality constrained optimization. The active set technique reduces the size of quadratic programming (QP) subproblem. While by the filter method, there is no penalty parameter estimate. Moreover, Maratos effect can be overcome by filter technique. Global convergence property of the proposed algorithm are established under suitable conditions. Some numerical results are reported in this paper.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.115-123
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• 1999

An optimization problem is to select the best of many possible design alternatives in a complex design space. Genetic algorithms, one of the numerous techniques to search optimal solution, have been successfully applied to various problems (for example, parameter tuning in expert systems, structural systems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with more conventional computational technique. But, conventional genetic algorithms are ill defined for two classes of problems, ie., penalty function and fitness scaling. Therefore, this paper develops Improved genetic algorithms(IGA) to solve these problems. As a case study, numerical examples are demonstrated to show the effectiveness of the Improved genetic algorithms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.3
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• pp.159-167
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• 2004

The objective of this study is development of the optimum design program to minimize the cost for PSC box girder bridge using the full staging method to indicate the necessity for the optimum design applied many types of bridges. It also considered the proper span length to girder depth ratio and the cell number along the width of bridge. This program used SUMT procedure and Kavlie's extended penalty function to allow infeasible design points in the process. Powell's direct method was used in searching design points and Gradient Approximate Method was used to reduce design hours. This study showed the convergence in design parameter and correlation of totally optimized cost according to cell numbers, span lengths, and lane numbers.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.4
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• pp.576-585
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• 2001

In this paper, we investigated the method of compensation for optical pulse shape distortion due to both chromatic dispersion and SPM(self phase modulation) in a single mode fiber We selected MSSI(mid-span spectral inversion) as compensation method using OPC(optical phase conjugator). We used EOP(eye-opening penalty) parameter in order to evaluate the efficiency of waveform distortion compensation. In this paper, we induced optimum pump power level in optical phase conjugator through analytic method of computer simulation. And we investigated input signal power range being able to maintain stable reception performance under the condition of optimum pump power. We verified the possibility of high performance optical transmission system realization through the inducement and application of optimum pump power, input signal power and in-line amplifier spacing, because power control is important in the compensation for optical pulse distortion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.181-186
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• 2022

Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.25-33
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• 2024

In tomographic image reconstruction, the focus is on developing CT image reconstruction methods that can maintain high image quality while reducing patient radiation exposure. Typically, statistical image reconstruction methods have the ability to generate high-quality and accurate images while significantly reducing patient radiation exposure. However, in cases like CT image reconstruction, which involve multi-dimensional parameter estimation, the degree of the Hessian matrix of the penalty function is very large, making it impossible to calculate. To solve this problem, the author proposed the PEMG-1 algorithm. However, the PEMG-1 algorithm has issues with the convergence speed, which is typical of statistical image reconstruction methods, and increasing the penalty log-likelihood. In this study, we propose a reconstruction algorithm that ensures fast convergence speed and monotonic increase in likelihood. The basic structure of this algorithm involves sequentially updating groups of pixels instead of updating all parameters simultaneously with each iteration.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.217-224
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• 2013

We developed a time-lapse inversion using new cross-model constraints based on change ratio and resolution of model parameters. The cross-model constraint based on change ratio imposes the same penalty on the model parameters with equal change ratio. This constraint can emphasize the model parameters with significant change regardless of their increase or decrease. The resolution cross-model constraint imposes a small penalty on the model parameters with poor resolution, but a large penalty on the model parameters with good resolution. Thus, the model parameter with poor resolution can be effectively identified in the inversion result if they are significantly changed with time. Through the numerical tests for 3D resistivity monitoring data sets, the performance of these two cross-model constraints was confirmed. Finally, for the safety estimation of a sea dyke, we applied the developed time-lapse inversion to the 3D resistivity monitoring data that were acquired at a sea dike located in western coastal area of Korea. The result of time-lapse inversion suggested that there were no significant changes at the sea dike during the monitoring period.