• Proceedings of the KSME Conference
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• 2003.04a
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• pp.845-850
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• 2003

The scattered light pattern from a machined surface generally contains much information concerning the surface roughness. The light pattern can be acquired by optical system and analyzed by statistical method. This kind of surface roughness measurement system can be easily adopted on the machine measurement. But the fully assembled system is too complex to implement on small systems using micro-controller. This study proposes the idea of reducing the number of optical components by removing screen and examines image processing of a light pattern to minimize the negative result of incomplete optical system. And the Gaussian blur filtering is concluded to be the best method of proposed measurement system. Furthermore light intensity variation of image pattern can be treated as a signal, therefore FIR filtering gives the similar result of Gaussian blur effect.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.729-733
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• 2003

The presence of local features within clusters incurred by multi-modal nature of data prohibits many conventional clustering techniques from working properly. Especially, the clustering of datasets with non-Gaussian distributions within a cluster can be problematic when the technique with implicit assumption of Gaussian distribution is used. Current study proposes a simple tandem clustering method composed of k-means type algorithm and hierarchical method to solve such problems. The multi-modal dataset is first divided into many small pre-clusters by k-means or fuzzy k-means algorithm. The pre-clusters found from the first step are to be clustered again using agglomerative hierarchical clustering method with Kullback- Leibler divergence as the measure of dissimilarity. This method is not only effective at extracting the multi-modal clusters but also fast and easy in terms of computation complexity and relatively robust at the presence of outliers. The performance of the proposed method was evaluated on three generated datasets and six sets of publicly known real world data.

• Journal of Magnetics
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• v.2 no.3
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• pp.105-109
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• 1997

The Preisach model neds a density function or Everett function for the hysterisis operator to simulate the hysteresis phenomena. To obtain the function, many experimental data for the first order transition curves are required. However, it needs so much efforts to measure the curves, especially for the hard magnetic materials. By the way, it is well known that the density function has the Gaussian distribution for the interaction axis on the Preisach plane. In this paper, we propose a simple technique to determine the distribution function or Everett function analytically. The initial magnetization curve is used for the distribution of the Everett function for the coercivity axis. A major, minor loop and the initial curve are used to get the Everett function for the interaction axis using the Gaussian distribution function and acceptable results were obtained.

• The KSFM Journal of Fluid Machinery
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• v.7 no.5 s.26
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• pp.7-12
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• 2004

Multi-path ultrasonic flowrate measuring technology is being received much attentions from a variety of industrial fields to exactly measure the flowrate. Multi-path ultrasonic flowmeter has much advantage since it has no moving parts and little pressure loss. It offers good accuracy, repeatability, linearity and turn-down ratio can be over 1:50. The present study investigates flowrate integration errors using weighting factors. A theoretical flow model uses power law to describe a fully developed velocity profiles and wall roughness is changed. Gaussian, Chebyshev, and Tailor methods are used to integrate line-average velocities. The obtained results show that Chebyshev method in 2, 4-path arrangement and Gaussian method in 3, 5-path arrangement are not affected for wall roughness changes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.993-1001
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• 2000

This research features a method that quantitatively evaluates the performance of edge detection algorithms. Contrary to conventional methods that evaluate the performance of edge detection as a function of the amount of noise added to he input image, the proposed method is capable of assessing the performance of edge detection algorithms based on chosen parameters that influence the performance of edge detection. We have proposed a quantitative measure, called average performance index, that compares the average performance of different edge detection algorithms. We have applied the method to the commonly used edge detectors, Sobel, LOG(Laplacian of Gaussian), and Canny edge detectors for noisy images that contain straight line edges and curved line edges. Two kinds of noises i.e, Gaussian and impulse noises, are used. Experimental results show that our method of quantitatively evaluating the performance of edge detection algorithms can facilitate the selection of the optimal dge detection algorithm for a given task.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1108-1118
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• 2019

Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FM-NLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-of-the-art NLM-based denoising algorithms.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.631-641
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• 2020

In the present work, an approach for the multiple time probabilistic characterization of the response of linear structural systems subjected to random non-Gaussian processes is presented. Its fundamental property is working directly on the multiple time probability density functions of the actions and of the response. This avoids of passing through the evaluation of the response statistical moments at multiple time or correlations, reducing the computational effort in a consistent measure. This approach is the extension to the multiple time case of a previously published dynamic Probability Transformation Method (PTM) working on a single evolution of the response statistics. The application to some simple examples has revealed the efficiency of the method, both in terms of computational effort and in terms of accuracy.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.552-560
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• 2011

In this paper, a new no-reference sharpness measure using frequency domain coefficients is proposed. Although most existing sharpness measures used pixel intensity to compute the blur degree, the proposed sharpness measure computes the sharpness using frequency coefficients. To assess the perceived sharpness of a given image, the image is re-blurred by a Gaussian low pass filter and a new quality measure function was defined using the frequency domain coefficients of the given image and the re-blurred image. To evaluate the proposed algorithms, TID2008 quality assessment database was used. Experimental results show that the proposed quality assessment method showed high correlation with the subjective scores.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.1
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• pp.65-72
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• 2012

Let T > 0 be given. Let $(C[0,T],m_{\varphi})$ be the analogue of Wiener measure space, associated with the Borel proba-bility measure ${\varphi}$ on ${\mathbb{R}}$, let $(L_{2}[0,T],\tilde{\omega})$ be the centered Gaussian measure space with the correlation operator $(-\frac{d^{2}}{dx^{2}})^{-1}$ and ${\el}_2,\;\tilde{m}$ be the abstract Wiener measure space. Let U be the space of all sequence $<c_{n}>$ in ${\el}_{2}$ such that the limit $lim_{{m}{\rightarrow}\infty}\;\frac{1}{m+1}\;\sum{^{m}}{_{n=0}}\;\sum_{k=0}^{n}\;c_{k}\;cos\;\frac{k{\pi}t}{T}$ converges uniformly on [0,T] and give a set function m such that for any Borel subset G of $\el_2$, $m(\mathcal{U}\cap\;P_{0}^{-1}\;o\;P_{0}(G))\;=\tilde{m}(P_{0}^{-1}\;o\;P_{0}(G))$. The goal of this note is to study the relationship among the measures $m_{\varphi},\;\tilde{\omega},\;\tilde{m}$ and $m$.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.597-609
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• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.