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Record W2220238161 · doi:10.1109/vppc.2015.7353000

Prototyping and Testing Power Electronics Systems Using Controller Hardware-In-the-Loop (HIL) and Power Hardware-In-the-Loop (PHIL) Simulations

2015· article· en· W2220238161 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)Université du Québec à Trois-Rivières
Fundersnot available
KeywordsHardware-in-the-loop simulationEmulationPower electronicsController (irrigation)DC motorPower (physics)Fault (geology)Field-programmable gate arrayComputer scienceKey (lock)Rapid prototypingEmbedded systemElectric power systemEngineeringComputer hardwareElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Prototyping and proper testing of the electrical control unit of electric vehicles under various load profiles and fault conditions is necessary to ensure maximum efficiency of the electric motors, batteries and power electronics. Hardware-in-the-loop simulation and power hardware-in-the-loop offer means to prototype and test various controllers while simulating key parts of the system. This paper describes these two methods and presents results and design principles of a brushless DC motor emulation using PHIL simulation.

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 categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.034
GPT teacher head0.258
Teacher spread0.224 · 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

Quick stats

Citations30
Published2015
Admission routes1
Has abstractyes

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