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Record W2547649308 · doi:10.1109/epe.2016.7695680

A hardware-efficient self-tuned output capacitor current and time constant estimator for indirect energy transfer converters based on dynamic voltage slope adjustment

2016· article· en· W2547649308 on OpenAlexaff
Shadi Dashmiz, Behzad Mahdavikhah, Aleksandar Prodić, Brent McDonald, Jeffrey Morroni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOxford Instruments (Canada)
Fundersnot available
KeywordsCapacitorEstimatorConvertersConstant currentConstant (computer programming)Time constantVoltageControl theory (sociology)Computer scienceEnergy (signal processing)Electronic engineeringCurrent (fluid)EngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

This paper introduces a novel hardware-efficient auto-tuned output capacitor current and time-constant estimator for indirect energy transfer converters, which is based on well-known principle of the utilization of an auxiliary RC circuit and time constant matching. To provide accurate current measurement and early fault detection of the system while avoiding complex calculations/hardware usually existing in other auto-tuned methods, the reconstruction of the time constant is performed through a simple detection of the polarity of the slope of the estimator voltage during the main switch off time. The effectiveness of the estimator has been experimentally verified with a 20 W boost-based prototype, demonstrating about 97% of accuracy in the instantaneous current and time-constant measurements.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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