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Record W1547917606

Dual-PDM control for parallel resonant inverter

2009· article· en· W1547917606 on OpenAlexaff
Abdelhalim Sandali, A. Chériti, Abdelhadi El Moudden

Bibliographic record

VenueEuropean Conference on Power Electronics and Applications · 2009
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsInverterRectifier (neural networks)Resonant inverterPower factorDual (grammatical number)Power (physics)Electronic engineeringControl theory (sociology)Power controlComputer sciencePulse-width modulationPulse-density modulationTopology (electrical circuits)EngineeringControl (management)Electrical engineeringPhysicsVoltagePulse (music)Pulse-amplitude modulation
DOInot available

Abstract

fetched live from OpenAlex

The application of Pulse Density Modulation (PDM) control to a rectifier-series resonant inverter set makes it possible to implement an ac/ac converter with good performances (size and efficiency). However its power factor decreases when the load power varies. We propose a Dual-PDM control strategy adapted to a rectifier — parallel resonant inverter set. This converter is obtained by application of the rules of the duality to a series-resonant-inverter. The main interests of this converter are a self power factor correction and a step-up load power. Dual-PDM control law is defined and converter modeling is carried out. Results are validated by simulation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2009
Admission routes1
Has abstractyes

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