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Record W2831715069 · doi:10.1109/tia.2018.2854270

Closed-Loop Control for a Rotational Core Loss Tester

2018· article· en· W2831715069 on OpenAlexafffund
Jemimah C. Akiror, R. Sudharshan Kaarthik, John Wanjiku, Pragasen Pillay, Arezki Merkhouf

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsHydro-QuébecConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsControl theory (sociology)WaveformReference frameController (irrigation)TransformerMagnetic fluxHarmonic analysisVector controlEngineeringComputer sciencePhysicsElectronic engineeringVoltageMagnetic fieldInduction motorFrame (networking)Electrical engineeringControl (management)

Abstract

fetched live from OpenAlex

In this paper, a flux density waveform controller is proposed for core loss measurements under rotating magnetic fields in the electrical steel. It is based on a vector control scheme in the synchronous dq reference frame. The proposed dq vector controller eliminates the need of commonly used harmonic compensation techniques; improving the execution and convergence times of the controller. Furthermore, it eliminates the need of filters and transformer isolators, which further reduces delays, thus improving the system's dynamic response. The performance of the controller is analyzed using simulations and validated experimentally. The proposed controller is experimentally shown to control the magnetic flux waveforms within 2% of the reference flux density signals at very high densities (up to 1.9 T) under sinusoidal conditions, and up to 1.2 T under nonsinusoidal conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.287
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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