Cover Image

Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer

Core size estimation of magnetic nanoparticles (MNPs) using magnetization curves has been reliably utilized to obtain a fast and simple size estimation technique compared to transmission electron microscopy. This estimation technique involves solving the inverse problem of the magnetization curve. H...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohd Mawardi, Saari, Mohd Herwan, Sulaiman, Nurul Akmal, Che Lah, Mohd Razali, Daud, Kiwa, Toshihiko
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/38999/1/Non-Regularized%20Reconstruction%20of%20Magnetic%20Moment%20Distribution.pdf
http://umpir.ump.edu.my/id/eprint/38999/2/Non-regularized%20reconstruction%20of%20magnetic%20moment%20distribution%20of%20magnetic%20nanoparticles%20using%20barnacles%20mating%20optimizer_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38999/
https://doi.org/10.1109/ICSSE58758.2023.10227174
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.38999
record_format eprints
spelling my.ump.umpir.389992023-11-14T03:02:45Z http://umpir.ump.edu.my/id/eprint/38999/ Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer Mohd Mawardi, Saari Mohd Herwan, Sulaiman Nurul Akmal, Che Lah Mohd Razali, Daud Kiwa, Toshihiko T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Core size estimation of magnetic nanoparticles (MNPs) using magnetization curves has been reliably utilized to obtain a fast and simple size estimation technique compared to transmission electron microscopy. This estimation technique involves solving the inverse problem of the magnetization curve. However, conventional methods, such as the singular value decomposition (SVD) or non-negative least squares (NNLS) algorithms, require a regularization threshold to mitigate the overfitting issues of an ill-conditioned problem. This prior information on the regularization requirement may lead to inaccurate magnetic moment reconstruction if the regularization degree is high due to broad distributions of the reconstructed magnetic moment. This research proposes a non-regularized reconstruction technique of magnetic moment distribution using the recent machine learning technique of the Barnacles Mating Optimizer (BMO) algorithm. A simulated magnetization curve of unimodal moment distributions from 1 mT to 1 T is used to minimize a model-free magnetic moment distribution. A reconstruction comparison among the BMO, Particle Swarm (PSO), Genetic Algorithm (GA), Sine Cosine Algorithm (SCA) optimizers, and NNLS method is presented. The magnetic moment reconstruction using the BMO algorithm shows significantly less noise and smooth distribution compared to the PSO and GA algorithms with fewer computation times. Furthermore, the constructed peaks' position matches the original distribution and shows comparable performance with the conventional NNLS algorithm. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38999/1/Non-Regularized%20Reconstruction%20of%20Magnetic%20Moment%20Distribution.pdf pdf en http://umpir.ump.edu.my/id/eprint/38999/2/Non-regularized%20reconstruction%20of%20magnetic%20moment%20distribution%20of%20magnetic%20nanoparticles%20using%20barnacles%20mating%20optimizer_ABS.pdf Mohd Mawardi, Saari and Mohd Herwan, Sulaiman and Nurul Akmal, Che Lah and Mohd Razali, Daud and Kiwa, Toshihiko (2023) Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer. In: Proceedings of 2023 International Conference on System Science and Engineering, ICSSE 2023, 27-28 August 2023 , Virtual, Ho Chi Minh City. pp. 533-536. (192135). ISBN 979-835032294-1 https://doi.org/10.1109/ICSSE58758.2023.10227174
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Mohd Mawardi, Saari
Mohd Herwan, Sulaiman
Nurul Akmal, Che Lah
Mohd Razali, Daud
Kiwa, Toshihiko
Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
description Core size estimation of magnetic nanoparticles (MNPs) using magnetization curves has been reliably utilized to obtain a fast and simple size estimation technique compared to transmission electron microscopy. This estimation technique involves solving the inverse problem of the magnetization curve. However, conventional methods, such as the singular value decomposition (SVD) or non-negative least squares (NNLS) algorithms, require a regularization threshold to mitigate the overfitting issues of an ill-conditioned problem. This prior information on the regularization requirement may lead to inaccurate magnetic moment reconstruction if the regularization degree is high due to broad distributions of the reconstructed magnetic moment. This research proposes a non-regularized reconstruction technique of magnetic moment distribution using the recent machine learning technique of the Barnacles Mating Optimizer (BMO) algorithm. A simulated magnetization curve of unimodal moment distributions from 1 mT to 1 T is used to minimize a model-free magnetic moment distribution. A reconstruction comparison among the BMO, Particle Swarm (PSO), Genetic Algorithm (GA), Sine Cosine Algorithm (SCA) optimizers, and NNLS method is presented. The magnetic moment reconstruction using the BMO algorithm shows significantly less noise and smooth distribution compared to the PSO and GA algorithms with fewer computation times. Furthermore, the constructed peaks' position matches the original distribution and shows comparable performance with the conventional NNLS algorithm.
format Conference or Workshop Item
author Mohd Mawardi, Saari
Mohd Herwan, Sulaiman
Nurul Akmal, Che Lah
Mohd Razali, Daud
Kiwa, Toshihiko
author_facet Mohd Mawardi, Saari
Mohd Herwan, Sulaiman
Nurul Akmal, Che Lah
Mohd Razali, Daud
Kiwa, Toshihiko
author_sort Mohd Mawardi, Saari
title Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
title_short Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
title_full Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
title_fullStr Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
title_full_unstemmed Non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
title_sort non-regularized reconstruction of magnetic moment distribution of magnetic nanoparticles using barnacles mating optimizer
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38999/1/Non-Regularized%20Reconstruction%20of%20Magnetic%20Moment%20Distribution.pdf
http://umpir.ump.edu.my/id/eprint/38999/2/Non-regularized%20reconstruction%20of%20magnetic%20moment%20distribution%20of%20magnetic%20nanoparticles%20using%20barnacles%20mating%20optimizer_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38999/
https://doi.org/10.1109/ICSSE58758.2023.10227174
_version_ 1822923807648645120
score 13.232414

Similar Items