• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.34-38
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• 1996

In this paper, a new fast voronoi divider for vector quantization (VQ) is introduced, which results from Theorem that the nearest vectors in the sense of minimum mean square error(MMSE) have almost the same mean values of their elements. An improved splitting method for a VQ codebook design using the fast voronoi divider is also presented. Experimental results show that the new method reduces the complexity of training a VQ codebook several times with a high signal to noise ratio(SNR) using an appropriate extensive parameter of codebook.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.447-456
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• 2007

On the basis of marginal likelihood of the residual vector which is free of nuisance mean parameters, we propose new Lagrange Multiplier seasonal unit root tests in seasonal autoregressive process. The limiting null distribution of the tests is the standardized ${\chi}^2-distribution$. A Monte-Carlo simulation shows the new tests are more powerful than the tests based on the ordinary least squares (OLS) estimator, especially for large number of seasons and short time spans.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.1
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• pp.124-135
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• 2004

An experimental study is performed turbulent flow behind a square cylinder by using 2-D PIV technique. The Reynolds number investigated are 10.000. 30.000 and 50,000. The mean velocity vector, time mean axial velocity turbulence intensity. kinetic energy and Reynolds shear stress behind the cylinder are measured, The numerical method used this study is a CFD code, STAR-CD. The numerical results are compared with these of experimental.

• Kyungpook Mathematical Journal
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• v.56 no.1
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• pp.273-293
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• 2016

In this paper, we obtain that every biharmonic non-degenerate hypersurfaces in semi-Euclidean space $E^5_s$ with constant scalar curvature of diagonal shape operator has zero mean curvature.

• Journal of Mechanical Science and Technology
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• v.19 no.12
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• pp.2270-2280
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• 2005

An experimental study is performed for turbulent swirling flow behind a circular cylinder using 2-D PIV technique. The Reynolds number investigated are 10,000, 15,000, 20,000 and 25,000. The mean velocity vector, time mean axial velocity, turbulence intensity, kinetic energy and Reynolds shear stress behind the cylinder are measured before and behind the round cylinder along the test tube. A comparison is included with non swirl flow behind a circular and square cylinder. The recirculation zones are showed asymmetric profiles.

• Communications of the Korean Mathematical Society
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• v.13 no.4
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• pp.825-838
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• 1998

We prove that if a real hypersurface with constant mean curvature of a complex space form satisfying ▽$_{ξ/}$S = 0 and Sξ = $\sigma$ξ for a smooth function $\sigma$, then the structure vector field ξ is principal, where S denotes the Ricci tensor of the hypersurface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.107-113
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• 2017

This paper provides a new approach for modeling of vector quantizer(VQ) followed by analysis and design of subband codecs with imbedded VQ's. We compute the mean squared reconstruction error(MSE) which depend on N the number of entries in each codebook, k the length of each codeword, and on the filter bank(FB) coefficients in subband codecs. We show that the optimum M-band filter bank structure in presence of pdf-optimized vector quantizer can be designed by a suitable choice of equivalent scalar quantizer parameters. Specific design examples have been developed for two different classes of filter banks, paraunitary and the biorthogonal FB and the 2 channel case. These theoretical results are confirmed by Monte Carlo simulation.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.662-674
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• 1997

In order to post process the vector-quantized images employing the theory of projections onto convex sets or the constrained minimization technique, the the projector onto QCS(quantization constraint set) as well as the filter that smoothes the lock boundaries should be investigated theoretically. The basic idea behind the projection onto QCS is to prevent the processed data from diverging from the original quantization region in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in the vector quantization are arbitrarilly shaped unless the vector quantization has a structural code book, the implementation of the projection onto QCS is very complicate. This paper mathematically analyzes the projection onto QCS from the viewpoit of minimizing the mean square error. Through the analysis, it has been revealed that the projection onto a subset of the QCS yields lower distortion than the projection onto QCS does. Searching for an optimal constraint set is not easy and the operation of the projector is complicate, since the shape of optimal constraint set is dependent on the statistical characteristics between the filtered and original images. Therefore, we proposed a hyper-cube as a constraint set that enables a simple projection. It sill be also shown that a proper filtering technique followed by the projection onto the hyper-cube can reduce the quantization distortion by theory and experiment.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.