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Dual CL/LLC DC/DC Resonant Circuit Modules For Step-up Power Interface in Microwave Magnetron Application

2022· article· en· W4377972219 on OpenAlexaff
Matthew Bakalian, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsCavity magnetronMicrowaveElectrical engineeringVoltageRLC circuitElectronic engineeringDual (grammatical number)EngineeringPower (physics)Topology (electrical circuits)PhysicsComputer scienceCapacitorTelecommunicationsThin film

Abstract

fetched live from OpenAlex

A double output DC/DC resonant converter topology that utilizes a dual CL/LLC resonant networks for microwave magnetron application is proposed in this paper. The proposed dual CL/LLC resonant converter is able to achieve soft-switching operation for all the semiconductor devices. The CL resonant network is used to provide the required high voltage gain for the magnetron, whereas the LLC resonant circuit network is used to provide the low voltage for the auxiliary supply for the magnetron (i.e. supply voltage for the filament). The descriptions and theoretical analysis of the proposed resonant converter will be discussed in this paper. Simulation results on a full-scale 300V/4kV design and hardware results on a laboratory-scale 50V/900V, 108kHz proof-of-concept prototype will be given to highlight the features of the proposed circuit.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.221
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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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