(J-KSIAM) Volume 24 Number 3 (September 2020 issue) TOC
글쓴이 : KSIAM
작성일 : 2020-09-28

Dear colleagues and researchers,

The Journal of the Korean Society for Industrial and Applied Mathematics (J-KSIAM) Volume24 Number3(September 2020 issue) has been posed on https://www.ksiam.org/archive or other information on the journal is available on the KSIAM website http://www.ksiam.org or https://www.ksiam.org/journal

The journal is one of Korea Citation Indexed (KCI) journals since 2007 and is indexed in Emerging Sources Citation Index (ESCI) since 2017.

Readers interested in the following articles may download each of articles free of charge from our website and authors are encouraged to submit a paper via the online submission site.

Sincerely yours,

Hi Jun Choe, Editor-in-Chief

Zhiming Chen, Hyeong-Ohk Bae, Tao Tang, Associate Editors-In-Chief

Jae Hoon Jung, Jaemyung Ahn, Wanho Lee, Managing Editors

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JKSIAM-v24n3 pp243-291

MULTI-BLOCK BOUNDARY VALUE METHODS FOR ORDINARY DIFFERENTIAL AND DIFFERENTIAL ALGEBRAIC EQUATIONS

S. E. OGUNFEYITIMI AND M. N. O. IKHILE

https://s3-ap-northeast-2.amazonaws.com/ksiam-editor/1601252742862-jksiam-2020v24p243.pdf

In this paper, multi-block generalized backward differentiation methods for numerical solutions of ordinary differential and differential algebraic equations are introduced. This class of linear multi-block methods is implemented as multi-block boundary value methods (MB$_2$VMs). The root distribution of the stability polynomial of the new class of methods are determined using the Wiener-Hopf factorization of a matrix polynomial for the purpose of their correct implementation. Numerical tests, showing the potential of such methods for output of multi-block of solutions of the ordinary differential equations in the new approach are also reported herein. The methods which output multi-block of solutions of the ordinary differential equations on application, are unlike the conventional linear multistep methods which output a solution at a point or the conventional boundary value methods and multi-block methods which output only a block of solutions per step. The MB$_2$VMs introduced herein is a novel approach at developing very large scale integration methods (VLSIM) in the numerical solution of differential equations.

JKSIAM-v24n3 pp293-303

TRANSIENT THERMOELASTIC STRESS ANALYSIS OF A THIN CIRCULAR PLATE DUE TO UNIFORM INTERNAL HEAT GENERATION

KISHOR R. GAIKWAD AND YOGESH U. NANER

https://s3-ap-northeast-2.amazonaws.com/ksiam-editor/1601252745455-jksiam-2020v24p293.pdf

The present work aims to analyzed the transient thermoelastic stress analysis of a thin circular plate with uniform internal heat generation. Initially, the plate is characterized by a parabolic temperature distribution along the $z$-direction given by $T=T_{0}(r,z)$ and perfectly insulated at the ends $z=0$ and $z=h$. For times $t>0$, the surface $r=a$ is subjected to convection heat transfer with convection coefficient $h_{c}$ and fluid temperature $T_{\infty}$. The integral transform method used to obtain the analytical solution for temperature, displacement, and thermal stresses. The associated thermoelastic field is analyzed by making use of the temperature and thermoelastic displacement potential function. Numerical results are carried out with the help of computational software PTC Mathcad Prime-3.1 and shown in figures.

JKSIAM-v24n3 pp305-320

A METHOD FOR SOLVING OF LINEAR SYSTEM WITH NORMAL COEFFICIENT MATRICES

M.GHASEMI KAMALVAND, B.FARAZMANDNIA, AND M.ALIYARI

https://s3-ap-northeast-2.amazonaws.com/ksiam-editor/1601252747992-jksiam-2020v24p305.pdf

This study aims to generalize MINRES-N2 method \cite{el}. It means that we tend to obtain an algorithm to transfer each normal matrix- that its eigenvalues belong to an algebraic curve of low degree $k$- to its condensed form through using a unitary similarity transformation. Then, we aim to obtain a method to solve a system of linear equations that its coefficient matrix is equal to such a matrix by utilizing it. Finally this method is compared to the well-known GMRES method through using numerical examples. The results obtained through examples show that the given method is more efficient than GMRES.

JKSIAM-v24n3 pp321-330

EXTRACTING INSIGHTS OF CLASSIFICATION FOR TURING PATTERN WITH FEATURE ENGINEERING

SEOYOUNG OH AND SEUNGGYU LEE

https://s3-ap-northeast-2.amazonaws.com/ksiam-editor/1601252749955-jksiam-2020v24p321.pdf

Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data $X$ whose fixel values are defined in $[-1,1]$, $X-X^3$ and $\nabla X$ would be more meaningful feature than $X$ to represent the interface and bulk region for a complex pattern image data. Therefore, we use $X-X^3$ and $\nabla X$ in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.