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Record W2057642653 · doi:10.1049/iet-map.2012.0374

Antenna design exploiting adjoint sensitivity‐based geometry evolution

2013· article· en· W2057642653 on OpenAlexaff
Massoud Ghassemi, Mohamed H. Bakr, Nagula Sangary

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsBlackberry (Canada)McMaster University
Fundersnot available
KeywordsSensitivity (control systems)Antenna (radio)Computer scienceGeometryMathematicsTopology (electrical circuits)Mathematical optimizationAlgorithmElectronic engineeringEngineeringTelecommunicationsCombinatorics

Abstract

fetched live from OpenAlex

The authors present a new approach for evolutionary antenna design. Through exploiting efficient adjoint sensitivity analysis techniques, the antenna structure evolves to better satisfy the design constraints. The coordinates of a selected number of control vertices are chosen as optimisation parameters thus enabling evolution to arbitrary shapes. The authors approach is illustrated through the design of a number of microstrip structures.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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

Citations84
Published2013
Admission routes1
Has abstractyes

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