• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1230-1239
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• 2018

We derive a factorization theorem for the jet mass distribution with a given $p^J_T$ for the inclusive production, where $p^J_T$ is a large jet transverse momentum. Considering the small jet radius limit ($R{\ll}1$), we factorize the scattering cross section into a partonic cross section, the fragmentation function to a jet, and the jet mass distribution function. The decoupled jet mass distributions for quark and gluon jets are well-normalized and scale invariant, and they can be extracted from the ratio of two scattering cross sections such as $d{\sigma}/(dp^J_TdM^2_J)$ and $d{\sigma}/dp^J_T $. When $M_J{\sim}p^J_TR$, the perturbative series expansion for the jet mass distributions works well. As the jet mass becomes small, large logarithms of $M_J/(p^J_TR)$ appear, and they can be systematically resummed through a more refined factorization theorem for the jet mass distribution.

• Current Optics and Photonics
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• v.3 no.4
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• pp.285-291
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• 2019

We have studied the behavior of light in the intermediate regime between a single nanoslit and an infinite nanoslit array. We first calculated the optical characteristics of a small number of nanoslits using finite element numerical analysis. The phase variance of the proposed nanoslit model shows a gradual phase shift between a single nanoslit and ideal nanoslit array, which stabilizes before the total array length becomes ${\sim}0.5{\lambda}$. Next, we designed a transmission-enhanced Fresnel zone plate by applying the phase characteristics from the small-number nanoslit model. The virtual-point-source method suggests that the proposed Fresnel zone plate with phase-invariant nanoslits achieves 2.34x higher transmission efficiency than a conventional Fresnel zone plate. Our report describes the intermediate behaviors of a nanoslit array, which could also benefit subwavelength metallic structure research of metasurfaces.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.75-87
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• 2009

We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, comparative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks. Performance of the proposed technique applied to image fusion experiments, is characterized in terms of Mutual Information (MI) as the output quality measure. The method is tested with C=2 input images. The results of the proposed scheme indicate a practical and attractive alternative to current multi-focus image fusion techniques.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.17-25
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• 2001

Traditional adaptive noise cancelling methods rely their performance on various interfering parameters, such as convergence speed, tracking ability, numerical stability, relative frequency characteristics between target and reverberation signals, and activity of the target. In this paper, an adaptive noise cancelling method is suggested, which Provides a successful tradeoff mon these factors. It is designed to work on the transform domain, adopts the Gram-Schmidt orthogonalization process, and is implemented by the escalator algorithm. The transform domain approach supports a tradeoff between the convergence speed and numerical cost. The proposed method is verified by applying a real-data collected in the shallow waters off the east coasts of korea. It is shown that it has a good reverberation-rejection capability even for the target signal with adjacent frequency components to those of the reverberation, and its performance is invariant for the activity of the target.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.767-776
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• 2020

GcGAN is a deep learning model to translate styles between images under geometric consistency constraint. However, GcGAN has a disadvantage that it does not properly maintain detailed content of an image, since it preserves the content of the image through limited geometric transformation such as rotation or flip. Therefore, in this study, we propose a new image-to-image translation method, MSGcGAN(Multi-Scale GcGAN), which improves this disadvantage. MSGcGAN, an extended model of GcGAN, performs style translation between images in a direction to reduce semantic distortion of images and maintain detailed content by learning multi-scale images simultaneously and extracting scale-invariant features. The experimental results showed that MSGcGAN was better than GcGAN in both quantitative and qualitative aspects, and it translated the style more naturally while maintaining the overall content of the image.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.20-26
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• 2021

In this paper, we studied the rotation angle estimation methods required for image alignment in an image recognition environment. In particular, a rotation angle estimation method applicable to a low specification embedded-based environment was proposed and compared with the existing method using complex moment. The proposed method estimates the rotation angle through similarity mathcing of the 1D projection profile along the radial axis after converting an image into polar coordinates. In addition, it is also possible to select a method of using vector sum of the projection profile, which more simplifies the calculation. Through experiments conducted on binary pattern images and gray-scale images, it was shown that the estimation error of the proposed method is not significantly different from that of complex moment-based method and requires less computation and system resources. For future expansion, a study on how to match the rotation center in gray-scale images will be needed.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.55-62
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• 2016

The least-mean fourth (LMF) algorithm is well known for its fast convergence and low steady-state error especially in non-Gaussian noise environments. Recently, there has been increasing interest in the least mean square (LMS) algorithms with variable step size. It is because the variable step-size LMS algorithms have shown to outperform the conventional fixed step-size LMS in the various situations. In this paper, a variable step-size LMF algorithm is proposed, which adopts an algebraic optimal step size as a variable step size. It is expected that the proposed algorithm also outperforms the conventional fixed step-size LMF. The superiority of the proposed algorithm is confirmed by the simulations in the time invariant and time variant channels.