• Bulletin of the Korean Mathematical Society
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• v.56 no.3
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• pp.621-629
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• 2019

In this paper, we present a local convergence analysis of a three point method with convergence order $1.839{\ldots}$ for approximating a locally unique solution of a nonlinear operator equation in setting of Banach spaces. Using weaker hypotheses than in earlier studies, we obtain: larger radius of convergence and more precise error estimates on the distances involved. Finally, numerical examples are used to show the advantages of the main results over earlier results.

• The Pure and Applied Mathematics
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• v.28 no.2
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• pp.187-198
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• 2021

This work presents an exposition of both the internal structure of derived category of an abelian category D^*(𝓐) and its contribution in solving problems, particularly in algebraic geometry. Calculation of some morphisms will be presented between objects in D^*(𝓐) as elements in appropriate cohomology groups along with their compositions with the help of Yoneda construction under the assumption that the homological dimension of D^*(𝓐) is greater than or equal to 2. These computational settings will then be considered under sheaf cohomological context with a particular case from projective geometry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4492-4507
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• 2021

In camera sensor networks (CSNs), in order to better identify the point, full-view problem requires capture any facing direction of target (point or intruder), and its coverage prediction and sensor density issues are more complicated. At present, a lot of research supposes that a large number of homogeneous camera sensors are randomly distributed in a bounded square monitoring region to obtain full-view rate which is close to 1. In this paper, we deduce the sensor density prediction model in heterogeneous deployed CSNs with arbitrary full-view rate. Aiming to reduce the influence of boundary effect, we introduce the concepts of expanded monitoring region and maximum detection area. Besides, in order to verify the performance of the proposed sensor density model, we carried out different scenarios in simulation experiments to verify the theoretical results. The simulation results indicate that the proposed model can effectively predict the sensor density with arbitrary full-view rate.

• Kyungpook Mathematical Journal
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• v.62 no.4
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• pp.699-713
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• 2022

This paper proposes a hybrid successive over-relaxation iterative method for the numerical solution of a nonlinear diffusion in a one-dimensional porous medium. The considered mathematical model is discretized using a computational complexity reduction scheme called half-sweep finite differences. The local truncation error and the analysis of the stability of the scheme are discussed. The proposed iterative method, which uses explicit group technique and modified successive over-relaxation, is formulated systematically. This method improves the efficiency of obtaining the solution in terms of total iterations and program elapsed time. The accuracy of the proposed method, which is measured using the magnitude of absolute errors, is promising. Numerical convergence tests of the proposed method are also provided. Some numerical experiments are delivered using initial-boundary value problems to show the superiority of the proposed method against some existing numerical methods.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.243-251
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• 2024

In the current work, a poro-thermoelastic half-space issue with temperature-dependent characteristics and an inclined load is examined in the framework of the three-phase-lag model (3PHL) while taking into account the effects of magnetic and gravity fields. The resulting coupled governing equations are non-dimensional and are solved by normal mode analysis. To investigate the impacts of the gravitational field, magnetic field, inclined load, and an empirical material constant, numerical findings are graphically displayed. MATLAB software is used for numerical calculations. Graphs are used to visualize and analyze the computational findings. It is found that the physical quantities are affected by the magnetic field, gravity field, the nonlocal parameter, the inclined load, and the empirical material constant.

• Communications of the Korean Mathematical Society
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• v.39 no.3
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• pp.585-593
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• 2024

Let 𝒦 be a ring. An additive map 𝖚^⋄ → 𝖚 is called Jordan involution on 𝒦 if (𝖚^⋄)^⋄ = 𝖚 and (𝖚𝖛+𝖛𝖚)^⋄ = 𝖚^⋄𝖛^⋄+𝖛^⋄𝖚^⋄ for all 𝖚, 𝖛 ∈ 𝒦. If Θ is a (non-zero) 𝜂-generalized derivation on 𝒦 associated with a derivation Ω on 𝒦, then it is shown that Θ(𝖚) = 𝛄𝖚 for all u ∈ 𝒦 such that 𝛄 ∈ Ξ and 𝛄² = 1, whenever Θ possesses [Θ(𝖚), Θ(𝖚^⋄)] = [𝖚, 𝖚^⋄] for all 𝖚 ∈ 𝒦.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.511-522
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• 2011

Two-dimensional shallow water model based on the cut cell and the adaptive mesh refinement techniques is presented in this paper. These two mesh generation methods are combined to facilitate modeling of complex geometries. By using dynamically adaptive mesh, the model can achieve high resolution efficiently at the interface where flow changes rapidly. The HLLC Reimann solver and the MUSCL method are employed to calculate advection fluxes with numerical stability and precision. The model was applied to simulate the extreme urban flooding experiments performed by the IMPACT (Investigation of Extreme Flood Processes and Uncertainty) project. Simulation results were in good agreement with observed data, and transient flows as well as the impact of building structures on flood waves were calculated with accuracy. The cut cell method eased the model sensitivity to refinement. It can be concluded that the model is applicable to the urban flood simulation in case the effects of sewer and stormwater drainage system on flooding are relatively small like the dam brake.

• Journal of the Korean earth science society
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• v.34 no.6
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• pp.524-535
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• 2013

Catastrophe risk models require the damage functions of each vulnerable item in inventory to estimate volcanic ash losses. The damage functions are used to represent the relation between damage factors and damage and also widely used in engineering and natural hazard studies to calculate the vulnerability. In most cases, damage functions are constructed as fragility or vulnerability curves, and researchers are confused by the similarities between them particularly when they perform interdisciplinary research. Thus, we aim to explain the similarities and differences between fragility and vulnerability curves and their relationship by providing case studies to construct them. In addition, we suggest a simple method to construct the damage functions between damage ratio and volcanic ash thickness using limited damage data. This study comes from the fact that damage functions are generally constructed using damage data. However, there is no available volcanic ash damage data in Korea, and not even enough volcanic disaster data to construct damage functions in the world, compared to other hazards. Using the method suggested in the study and the limited damage data from Japan and New Zealand, we construct Weibull-type functions or linear functions dependent of available data to calculate volcanic ash loss estimation, which we think need to be corrected to make it more suitable for inventory characteristics and environmental conditions in Korea.