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Record W2138298378 · doi:10.1109/ccece.2006.277315

Optimisation Énergétique par Logique Floue Dans un Moteur à Induction Triphasé

2006· article· fr· W2138298378 on OpenAlexaff
L. Vuichard, P. Schouwey, Mariyam Lakhal, M. Ghribi, Azeddine Kaddouri

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsComputer scienceFuzzy logicTorqueInduction motorControl theory (sociology)Artificial intelligenceEngineeringControl (management)PhysicsVoltage

Abstract

fetched live from OpenAlex

This article presents a new method of research into the flow which minimizes the losses in the three-phase induction machine. The method uses fuzzy logic to find optimal flow at the point of operation defined by the torque and speed of rotations. Simulation under Simulink made it possible to validate the approach suggested. The results obtained show the effectiveness of the method which can be easily adapted for any other machine without major complexity

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 categoriesMeta-epidemiology (narrow)
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 score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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