• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.223-231
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• 2013

This paper presents a time-domain Gauss-Newton full-waveform inversion method for the material profile reconstruction in heterogeneous semi-infinite solid media. To implement the inverse problem in a finite computational domain, perfectly-matchedlayers( PMLs) are introduced as wave-absorbing boundaries within which the domain's wave velocity profile is to be reconstructed. The inverse problem is formulated in a partial-differential-equations(PDE)-constrained optimization framework, where a least-squares misfit between measured and calculated surface responses is minimized under the constraint of PML-endowed wave equations. A Gauss-Newton-Krylov optimization algorithm is utilized to iteratively update the unknown wave velocity profile with the aid of a specialized regularization scheme. Through a series of one-dimensional examples, the solution of the Gauss-Newton inversion was close enough to the target profile, and showed superior convergence behavior with reduced wall-clock time of implementation compared to a conventional inversion using Fletcher-Reeves optimization algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.1-8
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• 2007

In this paper, research on joint optimization of the image spatial registration and the exposure compensation is conducted. The exposure compensation is performed in a frame work of the intensity compensation based on the polynomial approximation of the relationship between images. This compensation is jointly combined with the registration problem employing the Gauss-Newton nonlinear optimization method. In this paper, to perform for a simple and stable optimization, the block-coordinate method is combined with the Gauss-Newton optimization and extensively compared with the traditional approaches. Furthermore, regression analysis is considered in the compensation part for a better stable performance. By combining the block-coordinate method with the Gauss-Newton optimization, we can obtain a compatible performance reducing the computational complexity and stabilizing the performance. In the numerical result for a particular image, we obtain a satisfactory result for 10 repeats of the iteration, which implies a 50% reduction of the computational complexity. The error is also further reduced by 1.5dB compared to the ordinary method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2281-2285
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• 2011

An algorithm for extracting SPICE MOS level 2 model parameters for the high voltage MOSFET DC model is proposed. The optimization method for analyzing the nonlinear data of the current-voltage curve using the Gauss-Newton algorithm is proposed and the pre-process step for calculating the threshold voltage and the mobility is proposed. The drain current obtained from the proposed method shows the maximum relative error of 5.6% compared with the drain current of 2-dimensional device simulation for the high voltage MOSFET.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.109-116
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• 2014

An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.186-194
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• 2019

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1257-1268
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• 2011

The thermal behavior of wood exposed to the outdoors is influenced by solar absorptivity and longwave emissivity. However, it is difficult to measure that properties directly. Hence we estimate the values of the parameter by using the least-square optimization technique. Finally we report the results for the computation of the values of the parameters.

• Honam Mathematical Journal
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• v.32 no.2
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• pp.193-202
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• 2010

We investigate the estimation of the moisture transfer coefficients in porous media by optimization technique which minimizes the functional defined by the squares error of the numerical solution of an inverse diffusion problem from their experimental values of the moisture content at the some time-steps. In this paper, we solve a diffusion equation numerically by the control volume finite element methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1266-1272
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• 2014

We define the mobile robot navigation problem as an optimization problem to minimize the cost function with the pose error between the goal position and the position of a mobile robot. Using Gauss-Newton method for the optimization, the optimal speeds of the left and right wheels can be found as the solution of the optimization problem. Especially, the rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot using the Jacobian derived from the kinematic model. When the robot detects the obstacle using sensors, the sub-goal switching method is adopted for the efficient obstacle avoidance during the navigation. The performance was evaluated using the simulation and the simulation results show the validity of the proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.548-558
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• 2009

This paper attempts to develop an algorithm which optimizes the electric field through the so-called NURB(Non-Uniform Rational B-spline) curve in order to improve the insulation capacity. In particular, the NURB curve is a kind of interpolation curve that can be expressed by a few variables. The electric field of a conductor is computed by Charge Simulation Method(CSM) while that of a spacer by Surface Charge Method(SCM); this mixed calculation method is adopted for the electric field optimization. For calculation of the initial and optimal shapes, the Gauss-Newton method, which is quite easy to formulate and has slightly faster convergence rate than other optimization techniques, was used. The tangential electric field, the total electric field, and the product of the tangential electric field and area (Area Effect) were chosen as the optimization objective function by the average value of electric field for the determined initial shape.