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http://hdl.handle.net/11452/32614
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DC Field | Value | Language |
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dc.date.accessioned | 2023-05-10T13:33:14Z | - |
dc.date.available | 2023-05-10T13:33:14Z | - |
dc.date.issued | 2013-03 | - |
dc.identifier.citation | Derebaşı, N. (2013). “Giant magnetoimpedance effect: Concept and prediction in amorphous materials”. Journal of Superconductivity and Novel Magnetism, 26(4), Special Issue, 1075-1078. | en_US |
dc.identifier.issn | 1557-1939 | - |
dc.identifier.issn | 1557-1947 | - |
dc.identifier.uri | https://doi.org/10.1007/s10948-012-1923-4 | - |
dc.identifier.uri | http://hdl.handle.net/11452/32614 | - |
dc.description.abstract | Giant magneto impedance (GMI) effect was experimentally measured on as-cast, post-production and coated with chemical technique amorphous wire and ribbon materials consisted of varied chemical composition over a frequency range from 0.1 to 8 MHz under a static magnetic field between -8 and +8 kA/m. The results show that each amorphous sample has a certain operational frequency for which the GMI effect has maximum magnitude and the other parameters such as annealing and coating have a significant influence on the GMI effect. It is believed that the domain structure and wall mechanism in the material are responsible for this behaviour. A 3-node input layer, 1-node output layer artificial neural network (ANN) model with three hidden layers including 30 neurons and full connectivity between the nodes was developed. A total of 1600 input vectors obtained from varied treated samples was available in the training data set. After the network was trained, better results were obtained from the network formed by the hyperbolic tangent transfer function in the hidden layers, there was a sigmoid transfer function in the output layer and we predicted the GMI. Comparing the predicted values obtained from the ANN model with the experimental data indicates that a well-trained neural network model provides very accurate results. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Physics | en_US |
dc.subject | GMI effect | en_US |
dc.subject | Amorphous materials | en_US |
dc.subject | Domains | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Ribbons | en_US |
dc.subject | Wires | en_US |
dc.subject | Coated materials | en_US |
dc.subject | Magnetic domains | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Transfer functions | en_US |
dc.subject | Artificial neural network models | en_US |
dc.subject | Chemical compositions | en_US |
dc.subject | Giant magneto impedance effect | en_US |
dc.subject | GMI effects | en_US |
dc.subject | Neural network model | en_US |
dc.subject | Operational frequency | en_US |
dc.subject | Sigmoid transfer function | en_US |
dc.subject | Static magnetic fields | en_US |
dc.subject | Amorphous materials | en_US |
dc.title | Giant magnetoimpedance effect: Concept and prediction in amorphous materials | en_US |
dc.type | Article | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.wos | 000317014500062 | tr_TR |
dc.identifier.scopus | 2-s2.0-84876471638 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü. | tr_TR |
dc.relation.bap | 2009/29 | tr_TR |
dc.contributor.orcid | 0000-0003-2546-0022 | tr_TR |
dc.identifier.startpage | 1075 | tr_TR |
dc.identifier.endpage | 1078 | tr_TR |
dc.identifier.volume | 26 | tr_TR |
dc.identifier.issue | 4, Special Issue | en_US |
dc.relation.journal | Journal of Superconductivity and Novel Magnetism | en_US |
dc.contributor.buuauthor | Derebaşı, Naim | - |
dc.contributor.researcherid | AAI-2254-2021 | tr_TR |
dc.subject.wos | Physics, applied | en_US |
dc.subject.wos | Physics, condensed matter | en_US |
dc.indexed.wos | SCIE | en_US |
dc.indexed.wos | CPCIS | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.wos.quartile | Q3 (Physics, applied) | en_US |
dc.wos.quartile | Q4 (Physics, condensed matter) | en_US |
dc.contributor.scopusid | 11540936300 | tr_TR |
dc.subject.scopus | Magnetic Sensors; Electric Impedance; Ribbons | en_US |
Appears in Collections: | Scopus Web of Science |
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