• Bulletin of the Korean Mathematical Society
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• v.58 no.3
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• pp.721-727
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• 2021

The convex hull of a generic triple of boundary points has non-zero finite volume in complex hyperbolic 2-space.

• The Pure and Applied Mathematics
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• v.20 no.1
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• pp.51-57
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• 2013

In this paper, we introduce asymptotically quasi-$f-g$-nonexpansive mappings in convex normed vector spaces and consider approximating common fixed points of a sequence of asymptotically quasi-$f-g$-nonexpansive mappings in convex normed vector spaces.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.279-289
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• 1999

Given n points in the plane the planar convex hull prob-lem in that of finding which of these points belong to the perimeter of the smallest convex region (a polygon) containing all n points. Here we suggest two kinds of methods. First we present a new sequential method for constructing the pla-nar convex hull O(1.5n) time in the quadratic decision tree model. Second using the sequential method we suggest a new parallel algo-rithm which solve the planar convex hull O(1.5n/p) time on a maspar Machine (CREW-PRAM) with O(n) processors. Also when we run on a maspar Machine we achieved a 37. 156-fold speedup with 64 pro-cessor.

• Restorative Dentistry and Endodontics
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• v.17 no.1
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• pp.141-152
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• 1992

Four endodontic instruments from different manufacturers(K - file, Hedstrom - file, K - flex - file, Cavi - Endo) were used to shape 80 mesial canals of extracted posterior teeth. The instrument's ability to enlarge and shape the canals as the original canal shape was assessed by superimposing radiographs of canals obtained before and after canal shaping. The results were as follows 1. Hedstrom - file enlarged canals greater than K - file, K - flex - file, and Cavi - Endo(P<0.05). There are no differences in canal enlargement among K - flex, K - flex - file, and Cavi - Endo(P<0.05). 2. All the instruments showed no difference in canal enlargement between convex side and concave side of B point in proximal and clinical radiographic views, but at the concave side of C point canals were enlarged greater than at the convex side(proximal view of K - file: P<0.05, the others: P<0.01). 3. There was no difference m canal enlargement between convex side and concave side in proximal view of A and D points of K - file and K - flex - file(P<0.05). But in clinical view, canal enlargement at convex side of A point and concave side of D point was greater than that at concave side of A point and convex side of D point respectively. 4. Hedstrom - files showed greater canal enlargement at convex side of A points and concave side of D points (P<0.05 or P<0.01). Cavi - Endo showed no difference in canal enlargement between convex side and concave side at A and D points in proximal view(P<0.05). Bur canal enlargement was greater at concave side than at convex side of D point in clinical view. 5. K - file and Cavi - Endo showed no differences in canal enlargement between convex and concave side in proximal and clinical view (P<0.05). K - flex - file showed greater canal enlargement at concave side in proximal and clinical view(P<0.0:5). Hedstrom file showed no difference in canal enlargement between convex side and concave side in proximal view, but showed greater canal enlargement at concave side in clinical view(P<0.05).

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.2
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• pp.313-320
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• 2011

SPH(Smoothed Particle Hydrodynamics) is a gridless Lagrangian technique that is useful as an alternative numerical analysis method used to analyze high deformation problems as well as astrophysical and cosmological problems. In SPH, all points within the support of the kernel are taken as neighbours. The accuracy of the SHP is highly influenced by the method for choosing neighbours from all particle points considered. Typically a linked-list method or tree search method has been used as an effective tool because of its conceptual simplicity, but these methods have some liability in anisotropy situations. In this study, convex hull algorithm is presented as an improved method to eliminate this artifact. A convex hull is the smallest convex set that contains a certain set of points or a polygon. The selected candidate neighbours set are mapped into the new space by an inverse square mapping, and extract a convex hull. The neighbours are selected from the shell of the convex hull. These algorithms are proved by Fortran programs. The programs are expected to use as a searching algorithm in the future SPH program.

• Korean Journal of Mathematics
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• v.12 no.2
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• pp.107-115
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• 2004

In this paper, the new subclass denoted by $S_p({\alpha},{\beta},{\xi},{\gamma})$ of $p$-valent holomorphic functions has been introduced and investigate the several properties of the class $S_p({\alpha},{\beta},{\xi},{\gamma})$. In particular we have obtained integral representation for mappings in the class $S_p({\alpha},{\beta},{\xi},{\gamma})$) and determined closed convex hulls and their extreme points of the class $S_p({\alpha},{\beta},{\xi},{\gamma})$.

• East Asian mathematical journal
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• v.26 no.5
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• pp.693-702
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• 2010

We study one-step iteration process to approximate common fixed points of two nonexpansive mappings and prove some convergence theorems in convex metric spaces. Using the so-called condition (A'), the convergence of iteratively defined sequences in a uniformly convex metric space is also obtained.

• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.363-369
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• 2001

This paper proposes a convex hull algorithm for 2D patterns. The proposed algorithm is divided ito 2steps; candidate convex point extraction and final convex point extraction. First step removes as many points as possible that cannot be convex points using simple operation. Second step computes final convex hull of 2D patterns. This method accelerates execution time, since it consists of simple operations. Experimental results show that the proposed method is faster than other 2 methods in speed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.99-107
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• 2011

The feature extraction of asterias amurensis by using patterns is difficult to extract all the concave and convex features of asterias amurensis nor classify concave and convex. Concave and convex as important structural features of asterias amurensis are the features which should be found and the classification of concave and convex is also necessary for the recognition of asterias amurensis later. Accordingly, this study suggests the technique to extract the features of concave and convex, the main features of asterias amurensis. This technique classifies the concave and convex features by using the multi-directional linear scanning and form the candidate groups of the concave and convex feature points and decide the feature points of the candidate groups and apply convex hull algorithm to the extracted feature points. The suggested technique efficiently extracts the concave and convex features, the main features of asterias amurensis by dividing them. Accordingly, it is expected to contribute to the studies on the recognition of asterias amurensis in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1931-1940
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• 2014

In this paper, we propose a new hand shape recognition algorithm based on the geometric features using the convex-hull from the depth image acquired by Kinect system. Kinect is a camera providing a depth image and user's skeleton information and used for detecting hand region. In the proposed algorithm, hand region is detected in a depth image acquired by Kinect and convex-hull of the region is found. Boundary points caused by noise and unnecessary points for recognition are eliminated in the convex-hull that changes depending on hand shape. Hand shape is recognized by the sum of internal angle of a polygon that is matched with convex-hull reconstructed with selected boundary points. Through experiments, we confirm that proposed algorithm shows high recognition rate not only for five models but also those cases rotated.