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Record W1589079949

Teaching drive control using Energetic Macroscopic Representation — Summer schools

2011· article· en· W1589079949 on OpenAlexaffabout
Alain Bouscayrol, P. Barrade, Loïc Boulon, K. Chen, Yiming Cheng, Phillipe Delarue, Frédéric Giraud, Betty Lemaire‐Semail, Tony Letrouvé, Walter Lhomme, Pierre Sicard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsUnit (ring theory)Representation (politics)Control (management)Degree (music)Computer scienceMathematics educationControl unitArtificial intelligencePsychologyPhysicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The Energetic Macroscopic Representation (EMR) has been imitated in 2000 to define control schemes of electric drives. Since 2002 this graphical tool has been introduced to teach drive control in France, then Canada, Switzerland and more recently China. A first paper has described a 5-ECTS unit for initiation level in a Master degree. A second paper has described a 5-ECTS unit for an expert level in a Master degree. This paper is devoted to a 3-day EMR summer school in order to initiate scientists and engineers to this new graphical method. A specific programme has been developed to face the short time of this summer school, including simulation workshops.

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 score0.920
Threshold uncertainty score0.397

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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