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Record W4384305381 · doi:10.20906/sbse.v2i1.3063

Otimização de TSF analíticas para SRMs via Algoritmo Enxame de Partículas e Plataforma HIL

2022· article· pt· W4384305381 on OpenAlexaff
Gustavo Xavier Prestes, Filipe Pinarello Scalcon, Rodrigo Padilha Vieira

Bibliographic record

VenueAnais do ... Simpósio Brasileiro de Sistemas Elétricos · 2022
Typearticle
Languagept
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)TorqueParticle swarm optimizationPython (programming language)Switched reluctance motorComputer sciencePID controllerTorque ripplePhysicsDirect torque controlControl engineeringEngineeringAlgorithmArtificial intelligenceInduction motor

Abstract

fetched live from OpenAlex

This paper features a performance comparative study between analytical torque- sharing functions. The optimal conditions of Oon and Oov were obtained by particle swarm algorithm as method to drive optimization of three-phase switching reluctance motor. The aim is to assess the level of core losses and torque ripple in each TSF, as well as current controller performance in different speed conditions. In order to work out simulation process in less time, the non-linear model developed in Typhoon/Python environment was used to have real-time results.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.006
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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