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Record W1964313009 · doi:10.1109/apex.2007.357707

Novel Digital Controller Improves Dynamic Response and Simplifies Design Process of Voltage Regulator Module

2007· article· en· W1964313009 on OpenAlexaff
Eric Meyer, Feng Guang, Yan‐Fei Liu

Bibliographic record

VenueConference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · 2007
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsQueen's University
Fundersnot available
KeywordsControl theory (sociology)Transient responseVoltage regulatorController (irrigation)Transient (computer programming)Digital controlLinear regulatorProcess (computing)Computer scienceControl engineeringVoltageStep responseLoad regulationEngineeringRegulatorElectronic engineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

A novel digital controller is presented in this paper that significantly improves the dynamic response of a voltage regulator module (VRM). The controller uses a digital linear scheme during steady-state operation and an innovative non-linear scheme during transient load conditions. The proposed non-linear controller accurately calculates the optimal response to an arbitrary load step variation. This paper demonstrates the design procedure of a VRM using the proposed controller. Since the response to the load variation is predictable, an engineer is able to design (without iteration) a VRM that is guaranteed to meet a set of dynamic response criteria (voltage deviation, recovery time).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

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