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Record W1836391695 · doi:10.1109/pes.2003.1270371

A system-on-a-chip architecture for power signal processing

2004· article· en· W1836391695 on OpenAlexaff
C. Li, F.P. Dawson

Bibliographic record

Venue2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVHDLElectronic engineeringSynchronization (alternating current)Computer scienceHarmonicsFilter (signal processing)Field-programmable gate arrayChipSignal processingDigital signal processingEngineeringVoltageElectrical engineeringComputer hardwareTelecommunications

Abstract

fetched live from OpenAlex

The basic underlying signals of interest in power engineering applications, such as relaying protection, metering or synchronization, are fundamental components, sequence components or signals that indicate a change in the system state. This paper explores a system-on-a-chip architecture that can accommodate the needs of the power engineering community and can reduce the number of variable parameters and chip real estate. A novel dual filter scheme is presented to emulate the properties of a delay line. A variable sampling technique is used to adapt to input frequency variations. A multiinput multioutput finite input response filter cascaded with a median filter provides sequence component information in real time. All signal processing is done in synchronism with the line frequency. The synchronization circuit is insensitive to voltage sags or surges and harmonics. Simulation models prototyped in VHDL are investigated and used to verify the developed concepts. Finally, in-circuit tests are performed using an Altera FPGA chip.

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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

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 head0.004
GPT teacher head0.187
Teacher spread0.182 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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