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Record W1518046597 · doi:10.1109/isemc.1988.14089

Finite element method applied to shielding performance of enclosures

2003· article· en· W1518046597 on OpenAlexaff
Luc B. Gravelle, G.I. Costache

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsElectromagnetic shieldingEMIElectromagnetic interferenceEnclosureAttenuationFinite element methodElectromagnetic compatibilityGroundShieldShieldsAcousticsElectromagnetic fieldElectrical engineeringMagnetic fieldGasketComputer scienceElectronic engineeringEngineeringMechanical engineeringPhysicsOpticsStructural engineeringGeology

Abstract

fetched live from OpenAlex

The shielding effectiveness of enclosures is predicted by means of a computer simulation based on the finite-element technique. To characterize shielding performance, possible sources of electromagnetic interference (EMI), such as radiated EMI, conducted EMI, grounding, electrostatic discharge and environmental effects, along with their corresponding distances (near- or far-field) to the equipment under test (EUT), are examined. The analysis considers only the penetration of the magnetic field through the walls and apertures, since the electric field attenuation is high when a good conducting material is used for the shield. A finite-element method that is based on a magnetic vector-potential formulation is used; it can be applied to calculate the shielding effectiveness of any arbitrary selected enclosure. The ultimate purpose of this CAD tool is to establish guidelines regarding aperture spacing on the enclosure and to identify regions of high field intensity that must be avoided by designers when locating sensitive circuits.>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.239
Teacher spread0.220 · 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
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

Citations7
Published2003
Admission routes1
Has abstractyes

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