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Record W2289821862 · doi:10.1109/iemdc.2015.7409280

State-of-the-art high-speed switched reluctance machines

2015· article· en· W2289821862 on OpenAlex
Earl Fairall, Berker Bilgin, Ali Emadi

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsSwitched reluctance motorComputer scienceMagnetic reluctanceState (computer science)Variety (cybernetics)Work (physics)RangingFault toleranceFault (geology)Electrical engineeringEngineeringTelecommunicationsArtificial intelligenceMechanical engineeringOperating systemMagnetAlgorithm

Abstract

fetched live from OpenAlex

High speed machines are being used in an increasing number of applications. Low cost and fault tolerance enables the vision of switched reluctance machines providing greater value to a variety of industries in the future. This paper presents the state of the art of high speed switched reluctance machines. Machines which operate above 20,000RPM have been classified as high speed for this work. The research work presented investigates machines and their drives ranging from 100W to 300kW.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.238

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 head0.011
GPT teacher head0.206
Teacher spread0.194 · 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

Quick stats

Citations40
Published2015
Admission routes2
Has abstractyes

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