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Record W4389162505 · doi:10.1109/mele.2023.3320508

Electrical Machines in Electromagnetic Transient Simulations: Focusing on efficient and accurate models

2023· article· en· W4389162505 on OpenAlex

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.

Bibliographic record

VenueIEEE Electrification Magazine · 2023
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrificationElectronicsRenewable energyAutomationDroneTransient (computer programming)Electric motorElectric potential energyAutomotive engineeringElectricityEngineeringElectrical engineeringComputer scienceEnergy (signal processing)Mechanical engineering

Abstract

fetched live from OpenAlex

Electrical machines are extensively used in our everyday life. On the one hand, this can be seen in the rapid growth of generation from renewable energy sources such as wind, hydropower, tidal, etc. On the other hand, at the energy utilization and consumption end visible to most people, we are also witnessing revolutionary changes in many sectors, such as the electrification of all types of transportation, i.e., electric vehicles, industry-wide initiatives for more-electric aircraft and more-electric ships, military vehicles and defense systems, industrial automation, industrial and personal robots, medical devices and instruments, flying drones, electronic toys, and the multitude of household appliances and devices, all of which are designed and built with electric motors of various types and sizes.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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