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Record W1952525211 · doi:10.1109/iwipp.2015.7295960

Plenary sessions overview: SiC power devices aspects for high power density and system approach for a successful market implementation

2015· article· en· W1952525211 on OpenAlexaff
Peter Friedrichs

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsPower moduleElectrical engineeringPower electronicsElectric power systemEnergy storagePower semiconductor devicePower (physics)InterfacingConvertersElectronicsEngineeringComputer scienceVoltage

Abstract

fetched live from OpenAlex

In small to medium size surface combatants there is an increasing need for efficient energy usage, which depends upon power electronics operating in constrained spaces with sensitive equipment. The vision for next generation integrated power systems (NG-IPS) includes MVDC distribution with integrated power conversion, energy storage management and automated fault isolation and recovery. Power conversion and solid state protection technologies are the critical enablers to the accomplishment of the Navy's vision. Wide bandgap power semiconductors enable medium voltage power conversion and protection and simultaneously power dense, efficient and environmentally compatible power supplies. The need for MVDC power distribution is for reliable packaging of modules at high current levels in order meet the power processing demands. The need for ship service loads is a higher level of functionality within a module to reduce risks and costs of technology upgrades. The purpose of this paper is to focus in on three applications to shipboard NG-IPS and identify present and future packaging challenges: (1) low to medium horsepower drives for pumps and fans, (2) medium voltage power converters interfacing into a MVDC distribution system and (3) MVDC solid state protective devices (SSPDs).

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.170
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1700.092

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 head0.019
GPT teacher head0.271
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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