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Record W2161764869 · doi:10.1109/mwsym.1992.188125

Characterizing microwave planar circuits by coupled finite-boundary element method

2003· article· en· W2161764869 on OpenAlexaff
Ke‐Li Wu, Chen Wu, J. Litva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFinite element methodPlanarLossy compressionBoundary element methodMicrowaveBoundary (topology)DielectricElectronic circuitBoundary knot methodTopology (electrical circuits)Computer scienceCoupling (piping)Boundary value problemElectronic engineeringFerrite (magnet)Mathematical analysisMechanical engineeringElectrical engineeringEngineeringMathematicsStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

A general approach to the analysis of microwave planar structures, specifically intended to treat complicated geometries and dielectric load, is presented. The proposed approach is based upon the coupling of the finite element and boundary element methods. Using the method, only the complex media subdomains, which may consist of lossy or anisotropic materials, need to be treated using the finite element method. Elsewhere, the boundary element method is used on the boundary to take into account the circuit configuration. This general approach can handle a problem with mixed electric and magnetic walls, as well as complicated dielectric load such as ferrite materials. Computed and measured data for various complicated devices are compared, showing excellent agreement.>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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