MétaCan
Menu
Back to cohort
Record W2122438157 · doi:10.1109/tie.2010.2048836

A Generalized Technique of Modeling, Analysis, and Control of a Matrix Converter Using SVD

2010· article· en· W2122438157 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 Transactions on Industrial Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSingular value decompositionControl theory (sociology)Matrix (chemical analysis)Modulation (music)Matrix decompositionPower (physics)Computer scienceMathematicsElectronic engineeringAlgorithmEngineeringPhysicsControl (management)Eigenvalues and eigenvectorsArtificial intelligenceMaterials science

Abstract

fetched live from OpenAlex

In this paper, a new simple and complete technique of modeling and analysis of a matrix converter is presented based on the singular value decomposition (SVD) of modulation matrix. The proposed modeling method yields a new limitation between the matrix converter gain and its input power factor, which is more relaxed as compared to the limits reported so far in the literature. The SVD of the modulation matrix leads to a unified modulation technique which achieves the full capability of a matrix converter. It is shown that this approach is general and all other modulation methods established for a matrix converter are specific cases of this technique. The proposed modulation method can be used to obtain the maximum reactive power in the input of a matrix converter in applications such as wind turbine and microturbine generators, where the input reactive power control is necessary.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.742

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 integrity0.0000.001
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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