MétaCan
Menu
Back to cohort
Record W1547770549 · doi:10.1109/jestpe.2015.2436390

A Flicker-Free Single-Stage Offline LED Driver With High Power Factor

2015· article· en· W1547770549 on OpenAlexafffund
Peng Fang, Yan‐Fei Liu, Paresh C. Sen

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
FundersQueen's University
KeywordsRipplePower factorLED lampFlickerCapacitorLED circuitLight-emitting diodeElectrical engineeringElectronic engineeringVoltagePower (physics)Flyback transformerComputer scienceEngineeringShort circuitPhysicsTransformer

Abstract

fetched live from OpenAlex

A conventional offline single-stage light-emitting diode (LED) driver with a high power factor usually produces a significant twice-line-frequency ripple LED current, where the ripple LED current is presented as flickering to human eye. This paper introduces a ripple cancellation method to remove the twice-line-frequency voltage ripple for an offline single-stage LED driver with a power factor correction. Consequently, a dc LED current can be produced to achieve flicker-free LED driving performance. At the same time, the required storage capacitor for the proposed LED driver can be greatly reduced, and the circuit implementation to achieve ripple cancellation is simple. Thus, the overall cost of the proposed LED driver is low. The proposed LED driver also features high efficiency because of its power structure. A 35-W Flyback experimental prototype and a 10-W Buck-Boost experimental prototype have been built to validate the proposed design and demonstrate its optimal performance.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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
GenreMethods

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

Citations80
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Journal of Emerging and Selected Topics in Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207