Modeling of mean-field ising-hysteresis behavior: A support vector machine classification

Ferromagnetic hysteresis behavior is a lagging relation between magnetization and external magnetic field. This understanding is useful for designing highly efficient magnetic applications e.g., magnetic recording devices. A magnetic phase and hysteresis properties fluctuate clearly when encountered...

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Main Authors: Jutarop Reungyos, Bhusana Premanode, Prachya Kongtawelert, Yongyut Laosiritaworn
Format: Journal
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/53493
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-534932018-09-04T10:01:00Z Modeling of mean-field ising-hysteresis behavior: A support vector machine classification Jutarop Reungyos Bhusana Premanode Prachya Kongtawelert Yongyut Laosiritaworn Engineering Materials Science Physics and Astronomy Ferromagnetic hysteresis behavior is a lagging relation between magnetization and external magnetic field. This understanding is useful for designing highly efficient magnetic applications e.g., magnetic recording devices. A magnetic phase and hysteresis properties fluctuate clearly when encountered with thermal noise. This creates difficulties in predicting and modeling; and poses a very challenging problem. In this study, we propose to fit parameters and select the suitable kernel functions of Support Vector Machine, of which the main tasks are i) managing the relationship among hysteresis properties, temperature, magnetic field, and magnetic frequency, and ii) classifying the symmetries of hysteresis. The results present a new novel of classifying symmetric behavior of hysteresis with high accuracy. © 2014 Taylor & Francis Group, LLC. 2018-09-04T09:50:20Z 2018-09-04T09:50:20Z 2014-07-24 Journal 16078489 10584587 2-s2.0-84901477583 10.1080/10584587.2014.905157 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84901477583&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/53493
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Engineering
Materials Science
Physics and Astronomy
spellingShingle Engineering
Materials Science
Physics and Astronomy
Jutarop Reungyos
Bhusana Premanode
Prachya Kongtawelert
Yongyut Laosiritaworn
Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
description Ferromagnetic hysteresis behavior is a lagging relation between magnetization and external magnetic field. This understanding is useful for designing highly efficient magnetic applications e.g., magnetic recording devices. A magnetic phase and hysteresis properties fluctuate clearly when encountered with thermal noise. This creates difficulties in predicting and modeling; and poses a very challenging problem. In this study, we propose to fit parameters and select the suitable kernel functions of Support Vector Machine, of which the main tasks are i) managing the relationship among hysteresis properties, temperature, magnetic field, and magnetic frequency, and ii) classifying the symmetries of hysteresis. The results present a new novel of classifying symmetric behavior of hysteresis with high accuracy. © 2014 Taylor & Francis Group, LLC.
format Journal
author Jutarop Reungyos
Bhusana Premanode
Prachya Kongtawelert
Yongyut Laosiritaworn
author_facet Jutarop Reungyos
Bhusana Premanode
Prachya Kongtawelert
Yongyut Laosiritaworn
author_sort Jutarop Reungyos
title Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
title_short Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
title_full Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
title_fullStr Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
title_full_unstemmed Modeling of mean-field ising-hysteresis behavior: A support vector machine classification
title_sort modeling of mean-field ising-hysteresis behavior: a support vector machine classification
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84901477583&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/53493
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