• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.193-208
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• 2006

A modified BFGS algorithm for solving the unconstrained optimization, whose Hessian matrix at the minimum point of the convex function is of rank defects, is presented in this paper. The main idea of the algorithm is first to add a modified term to the convex function for obtain an equivalent model, then simply the model to get the modified BFGS algorithm. The superlinear convergence property of the algorithm is proved in this paper. To compared with the Tensor algorithms presented by R. B. Schnabel (seing [4],[5]), this method is more efficient for solving singular unconstrained optimization in computing amount and complication.

• Honam Mathematical Journal
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• v.43 no.2
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• pp.289-304
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• 2021

In this paper, we establish several geometric characterizations focusing on the relationship between the squared norm of the second fundamental form and the warping function of SCR-lightlike warped product submanifolds in an indefinite Kaehler manifold. In particular, we find an estimate for the squared norm of the second fundamental form h in terms of the Hessian of the warping function λ for SCR-lightlike warped product submanifolds of an indefinite complex space form. Consequently, we derive an optimal inequality, namely $${\parallel}h{\parallel}^2{\geq}2q\{{\Delta}(ln{\lambda})+{\parallel}{\nabla}(ln{\lambda}){\parallel}^2+\frac{c}{2}p\}$$, for SCR-lightlike warped product submanifolds in an indefinite complex space form. We also provide one non-trivial example for this class of warped products in an indefinite Kaehler manifold.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.675-691
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• 2002

The focus of this work is on the development of large-scale numerical optimization methods for optimal control of steady incompressible Navier-Stokes flows. The control is affected by the suction or injection of fluid on portions of the boundary, and the objective function of fluid on portions of the boundary, and the objective function represents the rate at which energy is dissipated in the fluid. We develop reduced Hessian sequential quadratic programming. Both quasi-Newton and Newton variants are developed and compared to the approach of eliminating the flow equations and variables, which is effectively the generalized reduced gradient method. Optimal control problems we solved for two-dimensional flow around a cylinder. The examples demonstrate at least an order-of-magnitude reduction in time taken, allowing the optimal solution of flow control problems in as little as half an hour on a desktop workstation.

• Korean Journal of Mathematics
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• v.6 no.1
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• pp.77-85
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• 1998

In this paper we study the Hessian at critical points of energy function on Teichm$\ddot{u}$ller space T(R) and apply it to the index of minimal surfaces.

• Honam Mathematical Journal
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• v.22 no.1
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• pp.83-90
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• 2000

We establish a sufficient condition for a simple closed curve in the Euclidean plain $\mathbb{R}^2$, the unit sphere $S^2$ or the hyperbolic plane $H^2$ to be the boundary of a metric ball.

• Honam Mathematical Journal
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• v.22 no.1
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• pp.107-111
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• 2000

We study Einstein warped product spaces. As a result, we prove the following: if M is an Einstein warped product space with base a compact 2-dimensional surface, then M is simply a Riemannian product space.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.298-302
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• 2010

This study proposes a differential method to analyze the properties of the topographic surface driven from lidar imagery. Although airborne lidar imagery provides elevation information rapidly, a sequence of extraction processes are needed to acquire semantic information about objects such as terrain, roads, trees, vegetation, and buildings. For the processes, the properties present in a given lidar data need to be analyzed. In order to investigate the geometric characteristics of the surface, this study employs eigenvalues of the Hessian matrix. For experiments, a lidar image containing university campus buildings with the point density of about 1 meter was processed and the results show that the approach is effective to obtain the properties of each land object Surface.

• Journal of Biomedical Engineering Research
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• v.36 no.4
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• pp.79-85
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• 2015

The purpose of this study is to develop an automatic algorithm for vessel segmentation in X-Ray angiogram using Random Forest (RF). The proposed algorithm is composed of the following steps: First, the multiscale hessian-based filtering is performed in order to enhance the vessel structure. Second, eigenvalues and eigenvectors of hessian matrix are used to learn the RF classifier as feature vectors. Finally, we can get the result through the trained RF. We evaluated the similarity between the result of proposed algorithm and the manual segmentation using 349 frames, and compared with the results of the following two methods: Frangi et al. and Krissian et al. According to the experimental results, the proposed algorithm showed high similarity compared to other two methods.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.1-8
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• 1996

Recently, much attention has been paid to problems which is concerned with voltage instability phenomena and much works on these phenomena have been made. In this paper, by substituting d $P_{k}$ d $e_{k}$ ( $v^{\rarw}$= e +j f) for $P_{k}$ in conventional load flow, direct method for finging the limit of voltage stability is proposed. Here, by using the fact that taylor se- ries expansion in .DELTA. $P_{k}$ and .DELTA. $Q_{k}$ is terminated at the second-order terms, constraint equation (d $P_{k}$ d $e_{k}$ =0) and power flow equations are formulated with new variables .DSLTA. e and .DELTA.f, so partial differentiations for constraint equation are precisely calculated. The fact that iteratively calculated equations are reformulated with new variables .DELTA.e and .DELTA.f means that limit of voltage stability can be traced precisely through recalculation of jacobian matrix at e+.DELTA.e and f+.DELTA.f state. Then, during iterative process divergence may be avoid. Also, as elements of Hessian mat rix are constant, its computations are required only once during iterative process. Results of application of the proposed method to sample systems are presented. (author). 13 refs., 11 figs., 4 tab.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.