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Record W1947722309 · doi:10.1109/ecce.2015.7310082

Design of a 2-D magnetizer with the consideration of the z-component of the magnetic field

2015· article· en· W1947722309 on OpenAlexaff
John Wanjiku, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsYoke (aeronautics)Electromagnetic shieldingMagnetic fieldElectromagnetic coilMagnetic flux leakageLeakage (economics)Magnetic fluxMaterials scienceShielding effectField (mathematics)Composite materialElectrical engineeringNuclear magnetic resonancePhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

A magnetizer design methodology that takes into account systematic errors such as the variation in flux density (B), and the z-component of the magnetic field (Hz), is proposed. The effect of the sample diameter and the effective length of the yoke, i.e. yoke depth on Hz are analysed. Experimental results at 1.5 T and 60 Hz showed a reduction of 81 %, 72 % and 30 % by a large magnetizer, shielding and reducing the yoke depth from 80 mm to 10 mm, respectively. This was in comparison to an unshielded compact magnetizer. Hz is also dependent on magnetic loading. Furthermore, at 2 T and 60 Hz, magnetic contributions dominated Hz such that the effectiveness of shielding and reducing the yoke depth decreased to 27 % and 4 %, respectively. To achieve these very high flux densities, the magnetizers were designed to be compact (sample diameter of ≤ 100 mm and narrow airgaps of ≤ 2 mm). This reduction in size increases the leakage field above and below the sample to the same level of magnitude as the applied field, which affects the measurement of the magnetic field (H). Two H-coil sizes with a sensitivity difference of 60 % are used to show that the measured H is independent of the coil size, but depends on the leakage field. Their measured core loss difference under pulsating and rotating fields was about 6 %.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.188
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
Admission routes1
Has abstractyes

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