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Record W2890624277 · doi:10.1109/compel.2018.8459935

Efficiency Optimization of a 7-Switch Flying Capacitor Buck Converter Power Stage IC Using Simulation and Geometric Programming

2018· article· en· W2890624277 on OpenAlexaff
Andrija Stupar, Michael Halamicek, Tom Moiannou, Aleksandar Prodić, Josh A. Taylor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapacitorNetwork topologyGeometric programmingConvertersBuck converterComputer sciencePower (physics)Electronic engineeringPoint (geometry)Topology (electrical circuits)Operating pointInductorElectrical engineeringEngineeringVoltageMathematics

Abstract

fetched live from OpenAlex

Recently, it has been shown that geometric programming, a class of convex optimization problems, can be used to optimize converters quickly and accurately. Some however, such at the seven-switch flying capacitor step-down converter (7SFC), designed to operate in three modes as three distinct topologies in order to maximize efficiency for the given operating point, cannot be modeled as geometric programs (GPs) on the converter level. This paper presents a hybrid optimization procedure, generalizable to other topologies, that uses a GP coupled to a fast circuit simulation to quickly optimize and design a 7SFC power stage IC for multiple operating points.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations9
Published2018
Admission routes1
Has abstractyes

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