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Record W3038390483 · doi:10.1049/iet-pel.2020.0128

Multichannel sequential display LED driver with optimal transient performance and efficiency via synchronous integral control

2020· article· en· W3038390483 on OpenAlexaff
Haifeng Wang, Tingshu Hu

Bibliographic record

VenueIET Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsKensington Health
Fundersnot available
KeywordsTransient (computer programming)IntegratorWaveformControl theory (sociology)Channel (broadcasting)Transient responseComputer sciencePulse-width modulationElectronic engineeringTopology (electrical circuits)VoltageLight-emitting diodeEngineeringControl (management)Electrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This study proposes a high performance multichannel sequential display light emitting diode (LED) driver with pulse width modulated dimming control. A synchronous integral control strategy is developed for achieving optimal transient performances for the channel voltages and ideal rectangular waveforms for the LED current, which will help to maintain high efficiency and extend the lifetime of the LEDs. By synchronous integral control, each channel has a corresponding integrator whose input and output are turned on and off synchronously with the LED string. The proposed driver topology and control strategy are supported by detailed analysis on stability and transient response during on‐time interval of a channel by using the averaged state‐space model. The effectiveness of the design method is demonstrated with simulation. A three‐channel LED driver is constructed to experimentally validate the high efficiency and high performance of the proposed topology and control strategy with desired LED current waveform and nearly constant channel voltages.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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
Published2020
Admission routes1
Has abstractyes

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