• Communications of the Korean Mathematical Society
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• v.21 no.4
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• pp.679-687
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• 2006

The purpose of this paper is to obtain a new common fixed point theorem by using a new contractive condition in intuitionistic fuzzy metric spaces. Our result generalizes and extends many known results in fuzzy metric spaces and metric spaces.

• The Pure and Applied Mathematics
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• v.28 no.2
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• pp.103-110
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• 2021

The aim of this paper is to prove a common fixed point theorem for two w-compatible maps in modular A-metric spaces. The main result is also illustrated by an example to demonstrate the degree of validity of our hypothesis.

• Nonlinear Functional Analysis and Applications
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• v.27 no.1
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• pp.45-58
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• 2022

In this paper, iterative algorithms for approximating a common fixed point of a countable family of multi-valued demicontractive maps in the setting of Hadamard spaces are presented. Under different mild conditions, the sequences generated are shown to strongly convergent and ∆-convergent to a common fixed point of the considered family, accordingly. Our theorems complement many results in the literature.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.14-22
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• 2014

An autonomous vehicle requires a technology of generating maps by recognizing surrounding environment. The recognition of the vehicle's environment can be achieved by using distance information from a 2D laser scanner and color information from a camera. Such sensor information is used to generate 2D or 3D maps. A 2D map is used mostly for generating routs, because it contains information only about a section. In contrast, a 3D map involves height values also, and therefore can be used not only for generating routs but also for finding out vehicle accessible space. Nevertheless, an autonomous vehicle using 3D maps has difficulty in recognizing environment in real time. Accordingly, this paper proposes the technology for generating 2D maps that guarantee real-time recognition. The proposed technology uses only the color information obtained by removing height values from 3D maps generated based on the fusion of 2D laser scanner and camera data.

• Communications of the Korean Mathematical Society
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• v.19 no.3
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• pp.507-517
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• 2004

Let (2,2,2,2) be ramification indices for the Riemann sphere. It is well known that the regular branched covering map corresponding to this, is the Weierstrass P function. Lattes [7] gives a rational function R(z)= ${\frac{z^4+{\frac{1}{2}}g2^{z}^2+{\frac{1}{16}}g{\frac{2}{2}}$ which is chaotic on ${\bar{C}}$ and is induced by the Weierstrass P function and the linear map L(z) = 2z on complex plane C. It is also known that there exist regular branched covering maps from $T^2$ onto ${\bar{C}}$ if and only if the ramification indices are (2,2,2,2), (2,4,4), (2,3,6) and (3,3,3), by the Riemann-Hurwitz formula. In this paper we will construct regular branched covering maps corresponding to the ramification indices (2,4,4), (2,3,6) and (3,3,3), as well as chaotic maps induced by these regular branched covering maps.

• Korean System Dynamics Review
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• v.6 no.1
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• pp.5-15
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• 2005

This paper investigates psychological differences between constructors and interpreters of causal maps. This paper argues that dissipation effects and dilution effects applies to those who are to interpret causal maps not to those who construct them. Dissipation effects are psychological tendency that people perceive causal effect as weak as the number of causal links increases. Dilution effects occur when people undervalue the strength of causal relation as the number of causal variables increases. Experimental results show that concentration effects opposite to the dissipation effects and dilution effects explain more correctly the perception of constructors of causal maps. This paper points out that this asymmetric psychological tendencies between constructors and interpreters of causal maps is the psychological source of the communication problems between systems thinkers and their clients.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.75.1-75.1
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• 2019

We introduce a novel method for reconstructing the projected dark matter mass maps of merging galaxy clusters by applying the convolutional neural network (CNN) to their weak lensing maps. We generate synthesized grayscale images from given weak lensing maps that preserve their averaged galaxy ellipticity. We then apply them to multi-layered CNN with architectures of alternating convolution and trans-convolution filters to predict the mass maps. We train our architecture with 1,000 Subaru/Suprime-Cam mock weak lensing maps, and our method have better mass map prediction than the Kaiser-Squires method with the following three aspects: (1) better pixel-to-pixel correlation, (2) more accurate finding of density peak position, and (3) free from mass-sheet degeneracy. We also apply our method to the HST weak lensing map of the El Gordo cluster and compare our result to the previous studies.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.17-25
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• 1996

This paper presents a GCP marking technique using scanned maps. Twelve maps with a scale of 1:250, 000 were scanned and stored as raster images. The distortion factors of scanned maps were modelled by polynomials. The coefficients of the polynomials were determined by a least squre fit. This technique does not require a bulky and expensive digitizing table so that it is suitable for a low-cost pre-processing system. The GCP marking using this technique showed a sufficient accuracy for KITSAT1, 2 narrow camera images.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1399-1408
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• 2012

The light source is an important visual component that empirically affects the color and illumination of graphic objects, and it is necessary to precisely store and appropriately employ the information of all light sources in the real world in order to obtain photo-realistic composition results. The information of real light sources can be accurately stored in HDR environment maps; however, it is impossible to create new environment maps corresponding to dynamic virtual light sources from a single HDR environment map captured under a fixed lighting situation. In this paper, we present a technique to dynamically generate well-matched information for arbitrarily selected virtual light sources using HDR environment maps created under predefined lighting position and orientation. Using the information obtained from light intensity and distribution analysis, our technique automatically generates HDR environment maps for virtual light sources via image interpolation. By applying the interpolated environment maps to an image-based lighting technique, we show that virtual light can create photo-realistically rendered images for graphic models.