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Record W2798527205 · doi:10.1109/cjece.2017.2785780

Monitoring ON-Resistance of MOSFET Devices in Real Time for SVPWM-VSI With Direct Compensation

2018· article· en· W2798527205 on OpenAlexvenueno aff
YongKeun Lee, Jongwang Kim

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
FundersNational Research Foundation
KeywordsInsulated-gate bipolar transistorMOSFETPower semiconductor deviceCompensation (psychology)Power MOSFETElectrical engineeringVoltageInverterPower (physics)Computer scienceElectronic engineeringEngineeringTransistor

Abstract

fetched live from OpenAlex

One of the major causes for the operation failure and/or malfunction in a voltage source inverter is from power semiconductor devices, such as MOSFET and IGBT. Especially, under harsh operating environments, the power devices face various mechanical/thermal challenges, which can increase the equipment/device failure rate and cause unexpected interruptions and/or serious safety issues. This paper focuses on estimating the ON-resistance of the MOSFET/IGBT devices in real time while operating in space-vector pulsewidth modulation mode to monitor the status on power MOSFET/IGBT devices in real time, hoping that it can avoid those unexpected interruptions for safety. Since the increase in ON-resistance of the power MOSFET is identified as the fault signature, it is worthwhile to measure ON-resistance to prevent the output voltage distortions and the amplitude reduction from the set reference voltage in advance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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
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

Citations3
Published2018
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Electrical and Computer EngineeringSame topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207