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Record W2170468711 · doi:10.1109/ias.1993.299042

Modelling and control of magnet power supply system with switch-mode ripple regulator

2002· article· en· W2170468711 on OpenAlexaff
Rung‐Huei Liang, S.B. Dewan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRippleFeed forwardControl theory (sociology)RegulatorVoltage regulatorComputer scienceSwitched-mode power supplyRectifier (neural networks)HarmonicControl systemBandwidth (computing)Power (physics)Control engineeringEngineeringVoltageControl (management)Electrical engineeringPhysicsTelecommunicationsArtificial neural network

Abstract

fetched live from OpenAlex

The dynamic modeling and control design of a newly proposed magnet power supply system are presented. This system is based on a conventional rectifier power supply with a series switch-mode ripple regulator (SMRR). It has been shown that this system offers excellent steady-state performance. A critical assessment of the dynamic performance of SMRR is presented here. Small signal dynamic models for the system are established. The function of the SMRR as a speed-up feedforward path during dynamic transients is analyzed. The regulator design criteria for the closed-loop system to achieve optimum performance are identified. The precise control of the magnet current can be achieved with several kHz regulation bandwidth and less than 10 ppm harmonic content. A systematic design procedure is outlined, and a design example is provided. Computer simulation and experimental results are presented to verify the theoretical analyses.>

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)0.000
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 score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.155
Teacher spread0.149 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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