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Record W2621332976 · doi:10.1109/jestpe.2017.2710348

Real-Time System-on-Chip Emulation of Electrothermal Models for Power Electronic Devices via Hammerstein Configuration

2017· article· en· W2621332976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsEmulationPower electronicsComputer scienceThyristorElectronicsElectronic engineeringSoftwareEmbedded systemPower (physics)Insulated-gate bipolar transistorChipEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Real-time emulation of device-level power electronics plays an essential role in many industrial applications for design and hardware-in-the-loop testing of system components and controllers. Due to the complexity and heavy computational burden of the real-time hardware implementation of the analytical and the numerical models, this paper proposes dynamic electrothermal behavioral models for diode, thyristor, and insulated-gate bipolar transistor based on the Hammerstein configuration. The power electronic device model parameters are extracted from the known device datasheets and implemented in Zynq System-on-Chip platform with accelerated processing for real-time application. An electromagnetic rail gun system has been employed as the study case to compare the performance of the proposed electrothermal behavioral models with that of off-line simulation models on SaberRD software.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.243
Teacher spread0.234 · 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