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...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 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 |
| _version_ | 1825815216626073600 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T13:10:12Z |
| format | Conference or Workshop Item |
| id | UMPir38999 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:10:12Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir389992023-11-14T03:02:45Z 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 (Published) https://doi.org/10.1109/ICSSE58758.2023.10227174 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| 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 |
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