• Honam Mathematical Journal
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• v.43 no.3
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• pp.417-431
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• 2021

In this study, we introduced the notions of rough statistical convergence and defined the set of rough statistical limit points of a sequence and obtained statistical convergence criteria associated with this set in 2-normed space. Then, we proved that this set is closed and convex in 2-normed space. Also, we examined the relations between the set of statistical cluster points and the set of rough statistical limit points of a sequence in 2-normed space.

• Korean Journal of Mathematics
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• v.32 no.1
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• pp.121-135
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• 2024

In this paper, we have presented rough ideal statistical convergence of sequence on neutrosophic normed spaces as a significant convergence criterion. As neutrosophication can handle partially dependent components, partially independent components and even independent components involved in real-world problems. By examining some properties related to rough ideal convergence in these spaces we have established some equivalent conditions on the set of ideal statistical limit points for rough ideal statistically convergent sequences.

• Honam Mathematical Journal
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• v.43 no.1
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• pp.47-58
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• 2021

In this paper, we introduce rough statistical convergence of difference double sequences in normed linear spaces as an extension of rough convergence. We define the set of rough statistical limit points of a difference double sequence and analyze the results with proofs.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.619-632
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• 2022

In this study, we examine rough ${\Delta}\mathcal{I}$-statistical convergence for difference sequences as an extension of rough convergence. We investigate the set of rough ${\Delta}\mathcal{I}$-statistical limit points of a difference sequence and analyze the results with proofs.

• Journal of applied mathematics & informatics
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• v.37 no.5_6
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• pp.459-467
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• 2019

In this work, via Orlicz functions, we have obtained a generalization of rough statistical convergence of asymptotically equivalent triple sequences a new non-matrix convergence method, which is intermediate between the ordinary convergence and the rough statistical convergence. We also have examined some inclusion relations related to this concept. We obtain the results are non negative real numbers with respect to the partial order on the set of real numbers.

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.201-209
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• 2020

We introduce and study some basic properties of rough I-convergent of triple sequence spaces and also study the set of all rough I-limits of a triple sequence spaces.

• Annals of Fuzzy Mathematics and Informatics
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• v.16 no.3
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• pp.337-361
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• 2018

We introduce and study some basic properties of rough $I_{\lambda}$-statistical convergent of weight g (A), where $g:{\mathbb{N}}^3{\rightarrow}[0,\;{\infty})$ is a function statisying $g(m,\;n,\;k){\rightarrow}{\infty}$ and $g(m,\;n,\;k){\not{\rightarrow}}0$ as $m,\;n,\;k{\rightarrow}{\infty}$ and A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g (A) limits of a triple sequence of Bernstein-Stancu polynomials.

• Korean Journal of Mathematics
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• v.6 no.2
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• pp.195-203
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• 1998

In [3], we showed that overintegration may be needed to obtain the optimal $H^1$ error rate for the $p$ version. In this paper, we examine the convergence of the $p$ version in practice, and comment on the implementation of the $p$ version in commercial codes. Also, we give an example of a problem with extremely rough coefficients, for which overintegration is necessary to obtain the optimal $H^1$ convergence rate.

• Tribology and Lubricants
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• v.39 no.1
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• pp.8-12
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• 2023

This study presents a molecular simulation of adhesion control between carbon nanotube (CNT) and Ag thin film deposited on silicon substrate. Rough and flat Ag thin film models were prepared to investigate the effect of surface roughness on adhesion force. Heat treatment was applied to the models to modify the adhesion characteristics of the Ag/CNT interface based on thermal wetting. Simulation results showed that the heat treatment altered the Ag thin film morphology by thermal wetting, causing an increase in contact area of Ag/CNT interface and the adhesion force for both the flat and rough models changed. Despite the increase in contact area, the adhesion force of flat Ag/CNT interface decreased after the heat treatment because of plastic deformation of the Ag thin film. The result suggests that internal stress of the CNT induced by the substrate deformation contributes in reduction of adhesion. Contrarily, heat treatment to the rough model increases adhesion force because of the expanded contact area. The contact area is speculated to be more influential to the adhesion force rather than the internal stress of the CNT on the rough Ag thin film, because the CNT on the rough model contains internal stress regardless of the heat treatment. Therefore, as demonstrated by simulation results, the heat treatment can prevent delamination or wear of CNT coating on a rough metallic substrate by thermal wetting phenomena.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.223-229
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• 2014

In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.