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Record W2322304365 · doi:10.1109/tia.2013.2292994

A Multifunctional Digital Controller for a High-Power-Factor Electronic Ballast Dimmable With Standard Phase-Cut Dimmers

2014· article· en· W2322304365 on OpenAlexaff
John Lam, Joanne Hui, Praveen Jain

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsDimmerBallastPower factorController (irrigation)Duty cycleEngineeringDigital controlElectrical engineeringPower (physics)Electronic engineeringControl theory (sociology)VoltageComputer sciencePhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper proposes a novel multifunctional controller for a dimmable high-power-factor single-switch electronic ballast that can be used with standard phase-cut dimmers. Conventional dimmable electronic ballasts lack the ability to 1) provide smooth dimming and maintain high power factor when phase-cut dimmers are used and 2) regulate the lamp power when the input line voltage varies. A digital controller with multiple control functions is proposed in this paper. The proposed controller allows high power factor to be achieved by controlling the duty cycle during dimming with standard phase-cut dimmers. On the other hand, when dimming is not required, the lamp power is regulated through the regulation of the dc-link voltage. Additional control functions, such as lamp low-power cutoff operation, and protection function are also implemented inside the digital controller. The descriptions of the controller operating principles and its logic flow diagrams are provided in this paper. PSIM simulation and experimental results are provided on a 13-W compact fluorescent lamp to highlight the merits of the proposed work.

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 score0.010

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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