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Record W2166349895 · doi:10.1109/tie.2010.2048831

An Accurate Inductance Profile Measurement Technique for Switched Reluctance Machines

2010· article· en· W2166349895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsInductanceSwitched reluctance motorControl theory (sociology)Nonlinear systemComputer scienceFinite element methodElectronic engineeringMagnetic reluctanceEngineeringMagnetControl (management)Electrical engineeringPhysicsVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

Accurate modeling of switched reluctance machines (SRMs) is necessary in order to achieve satisfactory control performance. Due to their highly nonlinear characteristics, the exact inductance profile for SRMs in one electrical period ought to be obtained. The purpose of this paper is to propose an accurate method to model the SRM magnetization characteristic, representing the accurate inductance profile, in order to achieve higher control performance. Furthermore, the innovative method proposed in this paper addresses a diverse way to minimize overall losses, compared to conventional methods. Instead of using a specific apparatus for measurement, the proposed method directly uses the saturation feature of the phase inductance. This paper discusses the advantages and improvements of the proposed method compared to conventional methods. Finally, the results computed by finite-element analysis are compared with the experimental results in terms of SRM magnetization characteristics.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
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 score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.254
Teacher spread0.222 · 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