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Record W2324512413 · doi:10.1109/ecce.2014.6953962

Dynamic physical limits of boost converters: A benchmarking tool for transient performance

2014· article· en· W2324512413 on OpenAlexaff
Ignacio Galiano Zurbriggen, Matias Anun, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvertersTransient (computer programming)Control theory (sociology)Benchmark (surveying)Operating pointTransient responseComputer scienceLimit (mathematics)Boundary (topology)Forcing (mathematics)VoltageTopology (electrical circuits)Electronic engineeringMathematicsEngineeringControl (management)

Abstract

fetched live from OpenAlex

The control of boost converters presents a special challenge due to the its non-minimum phase behaviour, forcing a slow transient response when traditional linear controllers are employed. In order to achieve enhanced dynamic response, more complex controllers have been proposed (e.g.: adaptive, non-linear, boundary, etc.), but the improvements introduced by each one of them cannot be objectively assessed in the absence of an optimal standard reference. This work introduces the theoretical physical limits of performance of boost converters, providing valuable insight into the behaviour of the topology and setting a strong benchmark point for the dynamic performance evaluation. The theoretical optimal transient response is found in a normalized geometrical domain and characterized by closed-form expressions, providing simple, intuitive, and general results valid for any combination of LC parameters and voltage levels. The characterization of the ideal transient is validated by experimental results demonstrated with a 50W boost converter operating near the physical limit of 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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.212
Teacher spread0.205 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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