• Journal of Ocean Engineering and Technology
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• v.25 no.5
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• pp.9-14
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• 2011

The divergence-free finite elements introduced in this paper are derived from Hermite functions, which interpolate stream functions. Velocity bases are derived from the curl of the Hermite functions. These velocity basis functions constitute a solenoidal function space, and the gradient of the Hermite functions constitute an irrotational function space. The incompressible Navier-Stokes equation is orthogonally decomposed into its solenoidal and irrotational parts, and the decoupled Navier-Stokes equations are then projected onto their corresponding spaces to form appropriate variational formulations. The degrees of the Hermite functions we introduce in this paper are bi-cubis, quartic, and quintic. To verify the accuracy and convergence of the present method, three well-known benchmark problems are chosen. These are lid-driven cavity flow, flow over a backward facing step, and buoyancy-driven flow within a square enclosure. The numerical results show good agreement with the previously published results in all cases.

• Journal of Magnetics
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• v.12 no.2
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• pp.59-63
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• 2007

Ferromagnet/Semiconductor heterostructures have attracted much attention because of their potential applications in spintronic devices. We investigated the electronic structures and magnetism of monolayer Fe on $CuGaSe_2(001)$ by using the all-electron full-potential linearized augmented plane-wave method within a generalized gradient approximation. We considered the monolayer Fe deposited on both the CuGa atoms terminated (CuGa-Term) and the Se atom terminated (Se-Term) surfaces of $CuGaSe_2(001)$. The calculated magnetic moment of the Fe atom on the CuGa-Term was about $2.90\;{{\mu}_B}$. Those of the Fe atoms on the Se-Term were in the range of $2.85-2.98\;{{\mu}_B}$. The different magnetic behaviors of the Fe atoms on two different surfaces were discussed using the calculated layer-projected density of states.

• Atmosphere
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• v.23 no.4
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• pp.377-387
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• 2013

Projections of changes in the low latitude atmospheric circulation under global warming are investigated using the results of the CMIP5 ensemble mean. For this purpose, 30-yr periods for the present day (1971~2000) and the end of the $21^{st}$ century (2071~2100) according to the RCP emission scenarios are compared. The wintertime subtropical jet is projected to strengthen on the upper side of the jet due to increase in meridional temperature gradient induced by warming in the tropical upper-troposphere and cooling in the stratosphere except for the RCP2.6. It is also found that a strengthening of the upper side of the wintertime subtropical jet in the RCP2.6 due to tropical upper-tropospheric warmings. Model-based projection shows a weakening of the mean intensity of the Hadley cell, an upward shift of cell, and poleward shift of the Hadley circulation for the winter cell in both hemispheres. A weakening of the Walker circulation, which is one of the most robust atmospheric responses to global warming, is also projected. These results are consistent with findings in the previous studies based on CMIP3 data sets. A weakening of the Walker circulation is accompanied with decrease (increase) in precipitation over the Indo-Pacific warm pool region (the equatorial central and east Pacific). In addition, model simulation shows a decrease in precipitation over subtropical regions where the descending branch of the winter Hadley cell in both hemispheres is strengthened.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.125-131
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• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.365-380
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• 2012

Non-negative matrix factorization (NMF) is a very efficient method to explain the relationship between functions for finding basis information of multivariate nonnegative data. The multiplicative update (MU) algorithm is a popular approach to solve the NMF problem, but it fails to approach a stationary point and has inner iteration and zero divisor. So the elementwisely alternating projected gradient (eAPG) algorithm was proposed to overcome the defects. In this paper, we use the fact that the equilibrium point is stable to prove the convergence of the eAPG algorithm. By using a classic model, the equilibrium point is obtained and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the eAPG algorithm are obtained, which can accelerate the convergence. In addition, the conditions, which satisfy that the non-zero equilibrium point exists and is stable, can cause that the algorithm converges to different values. Both of them are confirmed in the experiments. And we give the mathematical proof that the eAPG algorithm can reach the appointed precision at the least iterations compared to the MU algorithm. Thus, we theoretically illustrate the advantages of the eAPG algorithm.

• Nuclear Engineering and Technology
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• v.7 no.4
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• pp.277-284
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• 1975

A result of measurement for the relative dose distribution of neutron gamma mixed radiation field in the biological irradiation facility installed at TRIGA Mark-III reactor is described. The relative dose distributions of neutron-gamma mixed radiation field in the biological exposure room have been experimentally determined using a thermoluminescent dosimeter. Presented herein in graphical forms are the experimental results obtained. It as observed that the region commonly having the characteristics of rather homogeneous horizontal and lateral dose distributions is confined to the area bounded by the two planes horizontally parallel to the beam direction with heights of about 40 cm and 130 cm, respectively, at distances beyond 100 cm from the segmentary surface of the aluminum pool liner projected into the the exposure room, while other areas show a steeper gradient in dosage, especially the places adjacent to the segment of the aluminum pool liner and near the inner po${\gamma}$lion of the concrete walls of the exposure room.

• The Journal of the Korea Contents Association
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• v.3 no.4
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• pp.17-22
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• 2003

In this paper, we propose the new side-view face detection method in color images which contain faces over one. It uses color and the geometrical distance between nose and chin. We convert RGB to YCbCr color space. We extract candidate regions of face using skin color information from image. And then, the extracted regions are processed by morphological filter, and the processed regions are labeled. Also, we correct the gradient of inclined face image using projected character of nose. And we detect the inclined side-view faces that have right and left 45 tips by within via ordinate. And we get 92% detection rate in 100 test images.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.