MétaCan
Menu
Back to cohort
Record W2334528010 · doi:10.1109/tia.2016.2551683

Design of a Sinusoidally Wound 2-D Rotational Core Loss Setup With the Consideration of Sensor Sizing

2016· article· en· W2334528010 on OpenAlexafffund
John Wanjiku, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2016
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsSizingElectrical engineeringCore (optical fiber)Materials scienceElectronic engineeringControl theory (sociology)EngineeringComputer sciencePhysicsAutomotive engineeringOpticsChemistry

Abstract

fetched live from OpenAlex

The design of a two-dimensional rotational core loss setup that considers sensor sizing and the airflux leakage field is presented. The length of the flux density (B) coils is evaluated based on the magnetic degradation caused by holes used to locate the B-coils. The measured core loss is shown to be independent of the planar magnetic field (H) coil size, but depends on the location and the thickness of the enclosed core area. This determines the extent of the airflux leakage field in the measured field. This field links through the air close to the sample surface, and is shown to bias the shape, magnitude, and phase of the measured magnetic field. Core losses measured using three testers show that the airflux leakage field reduces with increasing magnetizer diametrical size. However, it is independent of the stack length in compact magnetizers. Finally, the performance of the proposed magnetizer is assessed at 60 Hz, 400 Hz, and 1 kHz.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicMagnetic Field Sensors TechniquesFrench-language works237,207