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Record W2152374327 · doi:10.1109/mwsym.2005.1517011

Tuning of microwave filters by extracting human experience using fuzzy logic

2005· article· en· W2152374327 on OpenAlexaff
V. Miraftab, Raafat R. Mansour

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium Digest, 2005. · 2005
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChebyshev filterFuzzy logicMicrowaveComputer scienceFilter (signal processing)Fuzzy electronicsElectronic engineeringFiltering theoryFuzzy setArtificial intelligenceEngineeringFuzzy numberComputer visionTelecommunications

Abstract

fetched live from OpenAlex

This paper introduces the use of fuzzy logic to capture human experience in tuning of microwave filters. The approach could be used for any microwave circuit tuning problem that involves human expert information. The fuzzy logic systems used in this approach are based on human expert decisions. A 4-pole Chebyshev filter is used to demonstrate the concept. The results prove the validity of the method.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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

Citations18
Published2005
Admission routes1
Has abstractyes

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Same venueIEEE MTT-S International Microwave Symposium Digest, 2005.Same topicFuzzy Logic and Control SystemsFrench-language works237,207