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Record W4229916648 · doi:10.1109/pcee.2004.8

A PC-Based Real-Time Parallel Simulator of Electric Systems and Drives

2005· article· en· W4229916648 on OpenAlexaff
Christian Dufour, J. Bélanger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsComputer scienceSimulationComputationReal-time simulationComputer architecture simulatorSoftwareEmbedded systemOperating system

Abstract

fetched live from OpenAlex

This paper presents the RT-LAB Electrical Drive Simulator technology along with practical applications. The RT-LAB[Opal-RT Technologies inc.] simulation software enables the parallel simulation of electrical circuit on clusters of PC running QNX or RT-Linux operating systems at sample time below 10 µs. Using standard Simulink models including SimPowerSystems models, RT-LAB build computation and communication tasks necessary to effectively make parallel simulation of electrical systems on low cost off-the-shelf PC technology. To accommodate the high bandwidth of electrical systems, the RT-LAB Electrical Drive Simulator comes with special Simulink-based modelisation tools, namely ARTEMIS[Opal-RT Technologies Inc.] and RT-Events[Opal-RT Technologies Inc.], that permits real-time simulation of electrical systems at practical time step of 10 µs but with sub-µs equivalent precision through the use of interpolation techniques.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.014
Threshold uncertainty score0.046

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.202
Teacher spread0.197 · 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
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

Citations41
Published2005
Admission routes1
Has abstractyes

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