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Record W4389832752 · doi:10.1002/9781119612490.ch5

SECONDARY‐SIDE IMPLEMENTATIONS IN ISOLATED DC–DC CONVERTERS

2023· other· en· W4389832752 on OpenAlexaff
Gerry Moschopoulos

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsWestern University
Fundersnot available
KeywordsConvertersRectifier (neural networks)DiodeElectrical engineeringVoltage doublerVoltageElectronic engineeringEngineeringComputer scienceVoltage sourceDropout voltage

Abstract

fetched live from OpenAlex

The secondary of most isolated DC–DC converters is typically implemented with some sort of diode rectifier to rectify the voltage from the secondary winding so that it can be filtered and made to be DC. This chapter reviews three different alternatives to the conventional secondary-side diode rectifiers: synchronous rectifiers (SRs), current doubler rectifiers, and implementations for multiple output rectifiers. It discusses the basic principles of each of the alternatives and gives implementation examples. SRs are MOSFETs with very low values of on-state resistance that are used as diodes at the secondary of power converters with low output voltage and high output current. A current doubler can be used as an alternative to the conventional center tapped diode rectifier configuration at the secondary of an isolated DC–DC converter in certain applications. The simplest and cheapest method of secondary post-regulation is to use small, commercially available linear voltage regulators for each output.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.010

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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