• Computers and Concrete
• /
• v.5 no.4
• /
• pp.375-388
• /
• 2008

Crack opening governs many transfer properties that play a pivotal role in durability analyses. Instead of trying to combine continuum and discrete models in computational analyses, it would be attractive to derive from the continuum approach an estimate of crack opening, without considering the explicit description of a discontinuous displacement field in the computational model. This is the prime objective of this contribution. The derivation is based on the comparison between two continuous variables: the distribution if the effective non local strain that controls damage and an analytical distribution of the effective non local variable that derives from a strong discontinuity analysis. Close to complete failure, these distributions should be very close to each other. Their comparison provides two quantities: the displacement jump across the crack [U] and the distance between the two profiles. This distance is an error indicator defining how close the damage distribution is from that corresponding to a crack surrounded by a fracture process zone. It may subsequently serve in continuous/discrete models in order to define the threshold below which the continuum approach is close enough to the discrete one in order to switch descriptions. The estimation of the crack opening is illustrated on a one-dimensional example and the error between the profiles issued from discontinuous and FE analyses is found to be of a few percents close to complete failure.

• The Korean Journal of Applied Statistics
• /
• v.30 no.3
• /
• pp.311-321
• /
• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Communications for Statistical Applications and Methods
• /
• v.25 no.2
• /
• pp.235-243
• /
• 2018

Many studies exist on the influence of one or few observations on estimators in a variety of statistical models under the "large n, small p" setup; however, diagnostic issues in the regression models have been rarely studied in a high dimensional setup. In the high dimensional data, the influence of observations is more serious because the sample size n is significantly less than the number variables p. Here, we investigate the influence of observations on the least absolute shrinkage and selection operator (LASSO) estimates, suggested by Tibshirani (Journal of the Royal Statistical Society, Series B, 73, 273-282, 1996), and the influence of observations on selected variables by the LASSO in the high dimensional setup. We also derived an analytic expression for the influence of the k observation on LASSO estimates in simple linear regression. Numerical studies based on artificial data and real data are done for illustration. Numerical results showed that the influence of observations on the LASSO estimates and the selected variables by the LASSO in the high dimensional setup is more severe than that in the usual "large n, small p" setup.

• Journal of the Korean Chemical Society
• /
• v.40 no.1
• /
• pp.20-27
• /
• 1996

A stable and accurate inverse method for extracting potential from spectroscopic data studied. The method is based on the Tikhonov regularization method to overcome the possible instability of nonlinear inverse problems using a priori smooth properties of the potential energy surface. The merit of this method is to treat the potential as continuous functions of the intermolecular coordinates instead of the conventional parameter fitting of restricted potential forms. Numerical study for the Ar-Ar show that from spectroscopic data the exact potential has been recovered whole region and the discrepancies by the dispersion force observed at the large distance between the exact and Morse potential or from RKR method can be eliminated by this method.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.25-32
• /
• 2004

We propose a hierarchical disparity estimation algorithm with edge-preserving energy-based regularization. Initial disparity vectors are obtained from downsampled stereo images using a feature-based region-dividing disparity estimation technique. Dense disparities are estimated from these initial vectors with shape-adaptive windows in full resolution images. Finally, the vector fields are regularized with the minimization of the energy functional which considers both fidelity and smoothness of the fields. The first two steps provide highly reliable disparity vectors, so that local minimum problem can be avoided in regularization step. The proposed algorithm generates accurate disparity map which is smooth inside objects while preserving its discontinuities in boundaries. Experimental results are presented to illustrate the capabilities of the proposed disparity estimation technique.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.12
• /
• pp.77-83
• /
• 1998

Generally a moving picture suffers from motion blur, due to relative motion between moving objects and the image formation system. The purpose of this paper is to propose teh model for the motion blur and the restoration method using the regularized iterative technique. In the proposed model, the boundary effect between moving objects and background is analyzed mathematically to overcome the limit of the spatially invariant model. And we present the motion-based image segmentation technique for the object-based image restoration, which is the modified version of the conventional segmentation method. Based on the proposed model, the restoration technique removes the motion blur by using the estimated motion parameter from the result of the segmentation.

• The Journal of Engineering Geology
• /
• v.17 no.2 s.52
• /
• pp.271-277
• /
• 2007

To find out the anomalous zone in cut slope composed of phyllite and shist, we performed resistivity tomography using a pole-dipole way. The electrical distribution that propagates from a current source in lower part of slope is measured by a potential electrode in upper part of slope. Apparent resistivity data are inverted with an iterative regularized inversion method to reconstruct 3D resistivity image. By comparing with the resistivity images in relation to each section, the images of anomalous zone correspond to their positions represented in cut slope. Therefore, the application of resistivity tomography in cut slope is useful to recognize the extension of anomalous zone.

• Journal of Computing Science and Engineering
• /
• v.10 no.2
• /
• pp.58-67
• /
• 2016

With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called "Truncated Kernel Projection Machine" that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.4
• /
• pp.764-771
• /
• 2005

As high-speed class broadband communication services have come in earnest broadband communication services are well regularized, utilizing not only wire but also wireless. One of wireless communication systems named B-WLL system, which provides high-speed broadband communication services, gives users inconvenience because it utilizes wire in the service area. In order to overcome this drawback, local wireless construction with ubiquitous environment has been considered in a new service demand area. We bring forth a scheme of cell Planning with 2 GHz frequency for wireless LAN construction which provides connection and mobility with no restrictions in time and locations.

• The KIPS Transactions:PartB
• /
• v.12B no.3 s.99
• /
• pp.343-348
• /
• 2005

The goal of this paper is to study the joint effect of factors of neural network teaming procedure. There are many factors, which may affect the generalization ability and teaming speed of neural networks, such as the initial values of weights, the learning rates, and the regularization coefficients. We will apply a constructive training algerian for neural network, then patterns are trained incrementally by considering them one by one. First, we will investigate the effect of these factors on generalization performance and learning speed. Based on these factors' effect, we will propose a joint method that simultaneously considers these three factors, and dynamically hue the learning rate and regularization coefficient. Then we will present the results of some experimental comparison among these kinds of methods in several simulated nonlinear data. Finally, we will draw conclusions and make plan for future work.