• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.139-155
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• 2000

An exclusive simulation study is conducted in computing means for order statistics in standard normal variate. Monte Carlo moments are used in Shapiro-Francia W' statistic computation. Finally, quantiles for Shapiro-Francia W' are generated. The study shows that in computing means for order statistics in standard normal variate, complicated distributions and intensive numerical integrations can be avoided by using Monte Carlo simulation. Lack of accuracy is minimal and computation simplicity is noteworthy.

• Korean Journal of Mathematics
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• v.28 no.2
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• pp.205-221
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• 2020

Paranormed spaces are important as a generalization of the normed spaces in terms of having more general properties. The aim of this study is to introduce a new paranormed space |𝜙_z|(p) over the paranormed space ℓ(p) using Euler totient means, where p = (p_k) is a bounded sequence of positive real numbers. Besides this, we investigate topological properties and compute the α-, β-, and γ duals of this paranormed space. Finally, we characterize the classes of infinite matrices (|𝜙_z|(p), λ) and (λ, |𝜙_z|(p)), where λ is any given sequence space.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.175-182
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• 2007

The support vector machine has been successful in many applications because of its flexibility and high accuracy. However, when a training data set is large or imbalanced, the support vector machine may suffer from significant computational problem or loss of accuracy in predicting minority classes. We propose a modified version of the support vector machine using the K-means clustering that exploits the information in class labels during the clustering process. For large data sets, our method can save the computation time by reducing the number of data points without significant loss of accuracy. Moreover, our method can deal with imbalanced data sets effectively by alleviating the influence of dominant class.

• 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.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.8-16
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• 2021

In this paper, locations of vertiports were selected and evaluated to operate urban air mobility (UAM) in the Seoul metropolitan area. Demand data were analyzed using the data from the survey of commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to identify the center of the cluster to as the location of vertiports, and using the silhouette technique, the accuracy and reliability of the clustering were evaluated. The locations of the selected vertiports were also identified using satellite maps to ensure that the locations of the selected vertiports were suitable for the actual vertiport location, and, if the location was not appropriate, final vertiports were selected through the repositioning process.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.1033-1043
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• 2004

We consider the simultaneous Bayesian estimation for the normal means based on different noninformative type hyperpriors in hierarchical model. We provide numerical example using the famous baseball data in Efron and Morris (1975) for illustration.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.27-31
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• 2013

In this paper, we propose a new quantization method using fuzzy C-means clustering(FCM) for lumbar ultrasound image recognition. Unlike usual histogram based quantization, our method first classifies regions into 10 clusters and sorts them by the central value of each cluster. Those clusters are represented with different colors. This method is efficient to handle lumbar ultrasound image since in this part of human body, the brightness values are distributed to doubly skewed histogram in general thus the usual histogram based quantization is not strong to extract different areas. Experiment conducted with 15 real lumbar images verified the efficacy of proposed method.

• Kyungpook Mathematical Journal
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• v.55 no.4
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• pp.909-931
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• 2015

This paper presents some new paranormed sequence spaces $X(r,s,t,p;{\Delta})$ where $X{\in}\{l_{\infty}(p),c(p),c_0(p),l(p)\}$ defined by using generalized means and difference operator. It is shown that these are complete linear metric spaces under suitable paranorms. Furthermore, the ${\alpha}$-, ${\beta}$-, ${\gamma}$-duals of these sequence spaces are computed and also obtained necessary and sufficient conditions for some matrix transformations from $X(r,s,t,p;{\Delta})$ to X. Finally, it is proved that the sequence space $l(r,s,t,p;{\Delta})$ is rotund when $p_n$ > 1 for all n and has the Kadec-Klee property.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.6
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• pp.680-687
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• 1988

When only global or local features of images are considered, the segmented results exhibit inevitable errors. To reduce these errors, first we divide the image into uniform and nonuniform regions by considering the local properties of the image. Next we obtain the segmented results by applying the Fuzzy C-means (FCM) algorithm to the picture and determining to which uniform reigons each pixel of the nonuniform regions belongs. To reduce the computational burden and memory required for the FCM algorithm, the equations used for FCM algorithm are modified. The performance of the proposed method is quantitatively compared to existing ones using only global or local features of the picture. Computer simualtion result shows that the segmented results obtained by applying the proposed method are superior to existing ones.

• Journal of Sensor Science and Technology
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• v.13 no.6
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• pp.446-453
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• 2004

A portable electronic tongue (E-Tongue) system using an array of ion-selective electrode (ISE) and personal digital assistants (PDA) for recognizing and analyzing food and drink have been designed. By the employment of PDA, the complex algorithm such as fuzzy c-means algorithm (FCMA) could be used in E-Tongue, PUMA could iteratively solve the cluster centers of pre-determined standard patterns. And the membership between the standard patterns and unknown pattern could be analyzed easily by the present E-Tongue combined with PDA.