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Record W2056243190 · doi:10.1109/apex.2007.357619

Continuous-Time Digital Signal Processing Based Controller for High-Frequency DC-DC Converters

2007· article· en· W2056243190 on OpenAlexaff
Zhenyu Zhao, Vadim Smolyakov, Aleksandar Prodić

Bibliographic record

VenueConference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPulse-width modulationDigital signal processingDigital controlConvertersController (irrigation)Electronic engineeringTransient (computer programming)VoltageComputer scienceControl theory (sociology)Power (physics)Transient responseBuck converterElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper introduces a digital dual-mode controller for low-power high-frequency dc-dc switch-mode power supplies (SMPS) suitable for on-chip implementation. In steady state the controller behaves as a conventional digital PWM controller, and during transients it utilizes continuous-time digital signal processing to achieve very fast transient response. The continuous time DSP is triggered by a sudden change of output voltage. Then it performs a charge-balance based algorithm to achieve voltage recovery through a single on-off action of the power switch. The effectiveness of the method is demonstrated on an experimental 5 V-to- 2V, 400 kHz, 2.5 W buck converter that recovers voltage in the time equivalent to 3 PWM switching cycles, approaching converter physical limitations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.203
Teacher spread0.196 · 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 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

Citations25
Published2007
Admission routes1
Has abstractyes

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