MétaCan
Menu
Back to cohort

Design-oriented EMC analysis of wiring systems

2020· preprint· en· W3095573571 on OpenAlexaff
A. Mori, Paolo Di Bartolomeo, M. Bandinelli, A Bonsignore, N. Muot, C. Girard, G. Prin, Jean‐Philippe Parmantier, Isabelle Junqua, S. Bertuol, Jérôme Morio, Giulio Antonini, Maria Denise Astorino, Charles Jullien

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsSafran Electronics (Canada)
FundersEuropean Commission
KeywordsPoint (geometry)Electromagnetic compatibilityComputer scienceStatistical analysisReliability engineeringEngineeringElectronic engineeringMathematics

Abstract

fetched live from OpenAlex

Modern aircraft hosts a large number of electric systems. Due to the transition to a More Electrical Aircraft, future platforms will use even more electric systems. This increases the complexity of the EWIS design from an EMC point of view. When possible, segregation between potentially interfering cables or bundles is a convenient solution to avoid EMC problems. Due to the huge number of installation configurations, the evaluation of the minimal segregation distance between two interacting bundles can be unfeasible. To overcome this situation, a specialized tool has been developed, based on a statistical approach for the analysis of the interaction between couples of bundles. In the paper, the different modules needed for this analysis are detailed and described. Some canonical analysis are then reported to show the capabilities of the tool.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.236
GPT teacher head0.365
Teacher spread0.129 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicProbabilistic and Robust Engineering DesignFrench-language works237,207