MétaCan
Menu
Back to cohort
Record W1511514212 · doi:10.1109/imtc.1997.603958

Real-time compensation of instrument transformer dynamics using frequency-domain interpolation techniques

2002· article· en· W1511514212 on OpenAlexaff
Benoît Boulet, L. Kadar, J. Wikston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsFrequency domainComputer scienceTransformerCompensation (psychology)Interpolation (computer graphics)Electronic engineeringDynamics (music)Control theory (sociology)AcousticsElectrical engineeringArtificial intelligenceEngineeringPhysicsVoltageComputer vision

Abstract

fetched live from OpenAlex

The dynamics of current and voltage transformers often limit the accuracy of measurements in high-voltage power systems. These dynamics may not be well known, but frequency-response data obtained on the instrument transformer may be available for a finite set of frequencies. A compensating digital filter that approximately inverts the instrument transformer's frequency response is calculated using frequency-domain interpolation techniques. The Power Quality Analyser makes use of the compensating filter technique for improved accuracy of its real-time power quality calculations and 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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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
GenreMethods

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

Citations13
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicModel Reduction and Neural NetworksFrench-language works237,207