• Communications of the Korean Mathematical Society
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• v.34 no.3
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• pp.771-782
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• 2019

We show that mappings preserving unit distance are close to two-isometries. We also prove that a mapping f is a linear isometry up to translation when f is a two-expansive surjective mapping preserving unit distance. Then we apply these results to consider two-isometries between normed spaces, strictly convex normed spaces and unital $C^*$-algebras. Finally, we propose some remarks and problems about generalized two-isometries on Banach spaces.

• The Pure and Applied Mathematics
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• v.10 no.3
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• pp.193-198
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• 2003

Let X and Y be n-dimensional Euclidean spaces with $n\;{\geq}\;3$. In this paper, we generalize a classical theorem of Bookman and Quarles by proving that if a mapping, from a half space of X into Y, preserves a distance $\rho$, then the restriction of f to a subset of the half space is an isometry.

• Journal of the Korean Mathematical Society
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• v.41 no.4
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• pp.667-680
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• 2004

We generalize a theorem of W. Benz by proving the following result: Let $H_{\theta}$ be a half space of a real Hilbert space with dimension $\geq$ 3 and let Y be a real normed space which is strictly convex. If a distance $\rho$ > 0 is contractive and another distance N$\rho$ (N $\geq$ 2) is extensive by a mapping f : $H_{\theta}$ \longrightarrow Y, then the restriction f│$_{\theta}$ $H_{+}$$\rho$/2// is an isometry, where $H_{\theta}$＋$\rho$/2/ is also a half space which is a proper subset of $H_{\theta}$. Applying the above result, we also generalize a classical theorem of Beckman and Quarles.

• Bulletin of the Korean Mathematical Society
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• v.40 no.1
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• pp.9-15
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• 2003

In this paper, we will deal with the Aleksandrov-Rassias problem. More precisely, we prove some theorems concerning the mappings preserving one or two distances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.433-444
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• 2024

Our study explores the connection between the Pythagorean theorem and the Fixed-point theorem in metric spaces. Both of which center around the concepts of distance transformations and point relationships. The Pythagorean theorem deals with right triangles in Euclidean space, emphasizing distances between points. In contrast, fixed-point theorems pertain to the points that remain unchanged under specific transformations thereby preserving distances. The article delves into the intrinsic correlation between these concepts and presents a novel study in Euclidean metric spaces, examining the relationship between contraction mapping and Pythagorean Right Triangles. Practical applications are also discussed particularly in the context of image compression. Here, the integration of the Pythagorean right triangle paradigm with contraction mappings results in efficient data representation and the preservation of visual data relation-ships. This illustrates the practical utility of seemingly abstract theories in addressing real-world challenges.

• Journal of Communications and Networks
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• v.16 no.3
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• pp.270-279
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• 2014

The Viterbi decoding algorithm, which provides maximum - likelihood decoding, is currently considered the most widely used technique for the decoding of codes having a state description, including the class of linear error-correcting convolutional codes. Two classes of nonbinary convolutional codes are presented. Distance preserving mapping convolutional codes and M-ary convolutional codes are designed, respectively, from the distance-preserving mappings technique and the implementation of the conventional convolutional codes in Galois fields of order higher than two. We also investigated the performance of these codes when combined with a multiple frequency-shift keying (M-FSK) modulation scheme to correct narrowband interference (NBI) in power-line communications channel. Themodification of certain detectors of the M-FSK demodulator to refine the selection and the detection at the decoder is also presented. M-FSK detectors used in our simulations are discussed, and their chosen values are justified. Interesting and promising obtained results have shown a very strong link between the designed codes and the selected detector for M-FSK modulation. An important improvement in gain for certain values of the modified detectors was also observed. The paper also shows that the newly designed codes outperform the conventional convolutional codes in a NBI environment.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.62-73
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• 2010

A bird is needed efficient conservation through habitat management, as the representative of an organism to evaluate the steady of complex ecosystem. So, this study will offer the useful basic data for preserving habitat from now on, as presenting a estimating model with the GIS program which selected factors effecting the habitat of a forest-dwelling bird in Changwon. As the resort of the survey, the number of forest-dwelling birds living in the 135 survey sites were 5 order, 15 family, 26 species and 922 individual. Also, as the result of making habitat analysis into a predict model, 'NDVI', 'Distance to valley', 'Distance to mixed forest' and 'Area of field' were significant and they had R-squares of 51.3%. Next, as the resort of researching the accuracy of Model, it was a reasonable prediction, as the correlation coefficient is 0.735 and MAPE is 20.7%, and a predict map of habitat was made with the model. This map could predict species diversity of no investigated areas and could be an useful basic data for preserving habitat, as an on-the-spot survey.