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Record W2324851963 · doi:10.1109/ecce.2014.6953973

Design considerations of 2-D magnetizers for high flux density measurements

2014· article· en· W2324851963 on OpenAlexaff
John Wanjiku, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsMagnetizationYoke (aeronautics)Flux (metallurgy)Variation (astronomy)Magnetic fluxMaterials scienceSample (material)Field (mathematics)Core (optical fiber)Magnetic fieldCondensed matter physicsPhysicsMathematicsComposite materialMechanicsThermodynamics

Abstract

fetched live from OpenAlex

The Fieldmetric measurement of 2-D core losses requires the use of orthogonal magnetic field H and flux density B sensors that are placed in a region of very high uniformity. It is difficult to achieve uniformity in the entire measurement region, in addition to the variation of B with magnetization directions. Therefore, there is a need to eliminate and/or reduce any contribution of a 2-D magnetizer to this variation, which contributes to systematic errors. Uniformity due to the magnetizer can only be controlled at the design stage. In that regard, four 2-D magnetizers are compared based on the variation of B with changes in magnetization directions. At high flux densities, any non-uniformity in B results in very H values, thus the errors due to the magnetizer are significant. Another effect of high non-uniformity is a reduction of the measurement area. It will be shown that the choice of the magnetizer design (hence the sample shape) significantly affects the variation of B with magnetization direction. In addition, the effect of the yoke depth in mitigating this problem is presented where it reduces this variation by 50%, for certain magnetizers.

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: Methods · Consensus signal: none
Teacher disagreement score0.432
Threshold uncertainty score0.348

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 head0.059
GPT teacher head0.248
Teacher spread0.188 · 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
GenreMethods

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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