• Journal of Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Journal of Ocean Engineering and Technology
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• v.13 no.4 s.35
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• pp.174-181
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• 1999

A form parameter method compounds the form parameters which define the hull geometric characteristics. This method can transform a hull form by changing the form parameters. The form parameter method is a hull define method without utilization of mother ships. However it is difficult to determine these form parameters. Thus, we are complemented the form parameter method using the neural networks. It is found that the form parameter method using the neural networks is efficient in hull form design by consideration of application examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.297-301
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• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.462-474
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• 2001

In this paper, Generalized Differential Quadrature Method is applied to the buckling analysis of built-up columns without or with stay plates. numerical analysis using GDQM is carried out for various boundary conditions(simply supported conditions, fixed conditions, fixed-simply supported conditions), dimensionless stiffness parameter and dimensionless inertia moment parameter. The accuracy and convergence of solutions are compared with exact solutions of Gjelsvik to validate the results of GDQM. Results obtained by this method are as follows. 91) This method can yield the accurate numerical solutions using few grid points. (2) The buckling load of built-up column increases as the dimensionless stiffness parameter decreases. (3) The effects of boundary conditions on the buckling load are not considerable as the dimensionless stiffness parameter increases. (4) The buckling load of built-up column increases due to the stay plate.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.1
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• pp.89-96
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• 1995

The application of the 4-pole parameter method with 1 - D theory is acceptable for intake system analysis. However, the limitaion appears during the analysis of complicated intake system since this method is developed based on the plane wave thoery. For the intake system analysis, the usage of BEM(Boundary Element Method) is introduced describing its disadvantage. To combine benefits of both method. a hybrid method is introduced. This hybrid method consists of the 4-pole parameter with I-D theory and BEM. The developed method is applied to an automobile intake system analysis to obtain the transmission loss.

• Journal of computational fluids engineering
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• v.15 no.2
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• pp.55-61
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• 2010

A dependence of weighting parameter in preconditioning method for solving low Mach number flow with incompressible flow nature is investigated. The present preconditioning method employs a finite-difference method applied Roe‘s flux difference splitting approximation with the MUSCL-TVD scheme and 4th-order Runge-Kutta method in curvilinear coordinates. From the computational results of benchmark flows through a 2-D backward-facing step duct it is confirmed that there exists a suitable value of the weighting parameter for accurate and stable computation. A useful method to determine the weighting parameter is introduced. With this method, high accuracy and stable computational results were obtained for the flow with low Mach number in the range of Mach number less than 0.3.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.441-449
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• 2018

Deep learning has been used for various processing centered on image recognition. One core algorithms of the deep learning, convolutional neural network is an deep neural network that specialized in image recognition. In this paper, we use a convolutional neural network to classify forest insects and propose an optimization method. Experiments were carried out by combining two weight initialization and six parameter update methods. As a result, the Xavier-SGD method showed the highest performance with an accuracy of 82.53% in the 12 different combinations of experiments. Through this, the latest learning algorithms, which complement the disadvantages of the previous parameter update method, we conclude that it can not lead to higher performance than existing methods in all application environments.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.4
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• pp.9-17
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• 1996

This paper develops a sequential updating method of the Euler parameter generalized coordinates for the machine kinematics and dynamics, The Newton's method is slightly modified so as to utilize the Jacobian matrix with respect to the virtual rotation instead of this with repect to the Euler parameters. An intermediate variable is introduced and the modified Newton's method solves for the variable first. Relational equation of the intermediate variable is then solved for the Euler parameters. The solution process is carried out efficiently by symoblic inversion of the relational equation of the intermediate variable and the iteration equation of the Euler parameter normalization constraint. The proposed method is applied to a kinematic and dynamic analysis with the Generalized Coordinate Partitioning method. Covergence analysis is performed to guarantee the local convergence of the proposed method. To demonstrate the validity and practicalism of the proposed method, kinematic analysis of a motion base system and dynamic analysis of a vehicle are carried out.

• Earthquakes and Structures
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• v.4 no.4
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• pp.341-363
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• 2013

Earthquake response calculation, parametric analysis and seismic parameter optimization of base-isolated structures are some critical issues for seismic design of base-isolated structures. To calculate the earthquake responses for such non-symmetric and non-classical damping linear systems and to implement the earthquake resistant design codes, a modified complex mode superposition design response spectrum method is put forward. Furthermore, to do parameter optimization for base-isolation structures, a graphical approach is proposed by analyzing the relationship between the base shear ratio of a seismic base-isolation floor to non-seismic base-isolation one and frequency ratio-damping ratio, as well as the relationship between the seismic base-isolation floor displacement and frequency ratio-damping ratio. In addition, the influences of mode number and site classification on the seismic base-isolation structure and corresponding optimum parameters are investigated. It is demonstrated that the modified complex mode superposition design response spectrum method is more precise and more convenient to engineering applications for utilizing the damping reduction factors and the design response spectrum, and the proposed graphical approach for parameter optimization of seismic base-isolation structures is compendious and feasible.