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Record W2152593554 · doi:10.1109/eurcon.2007.4400646

Computer Aided Testing of Electrical Machines

2007· article· en· W2152593554 on OpenAlexaff
Marius Biriescu, Voicu Groza, Vladimir Creţu, Octavian Proștean, Gheorghe Madescu, Marţian Moţ

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAperiodic graphComputer scienceTest benchAutomatic test equipmentSoftwarePower (physics)TorqueElectric motorControl engineeringElectrical engineeringEmbedded systemEngineeringReliability engineering

Abstract

fetched live from OpenAlex

The paper presents a computer controlled platform that authors devised and built for testing electrical machines both in laboratory and in industrial environments. The hardware and software architecture of the system were developed to address the testing specifications required by standards or requested by customers. Also, the results of several significant measurements that were performed with this automatic test-bench are presented. The testing platform was employed for testing electrical machines in various settings like mixed-frequency alternate current regime, amplitude modulated quasi-periodic regime, aperiodic transitory regime, to name just the most relevant ones. Prototype tests were carried out over medium power induction motors and synchronous machines, and the results of these measurements are presented here.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.214
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations3
Published2007
Admission routes1
Has abstractyes

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