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Record W1998145181 · doi:10.1109/tmag.2012.2198208

A Novel Two-Axis Theory-Based Approach Towards Parameter Determination of Line-Start Permanent Magnet Synchronous Machines

2012· article· en· W1998145181 on OpenAlexaff
K. Lakshmi Varaha Iyer, Xiaomin Lu, Kaushik Mukherjee, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Magnetics · 2012
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceMagnetLine (geometry)Estimation theoryTorqueControl theory (sociology)AlgorithmMechanical engineeringPhysicsArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Understanding the importance and the scanty availability of research literature on parameter estimation of line-start permanent magnet synchronous machine (LSPMSM), this paper proposes a novel and yet fundamental approach towards parameter estimation. Experiments have been performed on a laboratory LSPMSM based on the proposed <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> axis methodology and measurements have been validated by analyzing the performance of the machine under different conditions using a developed computer program and an experimental setup. Dynamic and steady state performance analyses have been subsequently performed using the determined parameters and the results have been presented under direct online (DOL) starting and a sudden increase in load torque.

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: Methods · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.935

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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
GenreMethods

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

Citations25
Published2012
Admission routes1
Has abstractyes

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