MétaCan
Menu
Back to cohort
Record W2098468453 · doi:10.1109/ias.1991.178341

Design of a 3-phase controlled rectifier system employing open-loop predictive ride-through control

2002· article· en· W2098468453 on OpenAlexaff
E.P. Nowicki, S.B. Dewan, A.S. Sedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRectifier (neural networks)Control theory (sociology)Model predictive controlComputer scienceTerm (time)Line (geometry)Control engineeringControl (management)Control systemLoop (graph theory)Power (physics)Open-loop controllerClosed loopEngineeringArtificial neural networkArtificial intelligenceMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

When designing a three-phase controlled rectifier system, the design engineer must determine how the system will respond to line disturbances. It is shown how this problem can be effectively solved by a control strategy, which is referred to as open-loop predictive ride-through control. This control technique provides the system with inherent ride-through capability, and thus eliminates the need for an interruptible power supply, where short-term line disturbances are a problem. This technique is effective for both continuous and discontinuous operation modes, as well as for static and dynamic loads. Design equations are derived, and a design example is given to illustrate the practicality of this control strategy.>

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.307
Teacher spread0.251 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Battery Technologies ResearchFrench-language works237,207