MétaCan
Menu
Back to cohort
Record W2907107033 · doi:10.1109/peac.2018.8590548

Operation of Combined HF and LF Boost Stage Inductors in PFC Design

2018· article· en· W2907107033 on OpenAlexaff
Ken King Man Siu, Carl Ngai Man Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInductorBoost converterElectronic engineeringComputer scienceEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents an alternative solution on boost stage inductor design. The presented alternative inductor solution is to use an addition LC circuit to parallel with an original converter inductor so that it results an effective converter inductor. High frequency and low frequency current components in a traditional boost stage inductor can be separated out individually. The inductor in LC circuit guides the high frequency current components and the original inductor only needs to handle the line frequency current component. Therefore, each inductor can be more specifically focused on their target frequency component to be the optimization. Thus, a more effective converter solution is resulted under the combined inductor solution. In this paper, a detail design criteria is presented and together with a comparison with different magnetic components is provided. The performance of the presented solution is experimentally verified in a 300W test platform. Both steady state and transient state experimental results also show with a good agreement with the theoretical findings.

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 score0.003
Threshold uncertainty score0.011

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.218
Teacher spread0.203 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207