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The Analysis of Linear Periodically-Time-Variable Circuits by the Frequency Symbolic Method with Application the D-Trees Method

2019· article· en· W3000719721 on OpenAlexaff
Yuriy Shapovalov, Dariya Bachyk, Ksenia Detsyk, Іван Шаповалов

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngine and Fuel Emissions
Canadian institutionsQueen's University
Fundersnot available
KeywordsSymbolic data analysisElectronic circuitFrequency domainVariable (mathematics)Computer scienceMathematicsNetwork analysisMatrix (chemical analysis)Algebraic equationAlgorithmTheoretical computer scienceMathematical analysisEngineeringNonlinear systemElectrical engineering

Abstract

fetched live from OpenAlex

In this paper we analyze the application of the matrix equation of L. A. Zadeh to symbolic analysis of the LPTV circuits in the frequency domain. The symbolic solution of the linear algebraic equations is proposed to be performed by the socalled the d-trees method. The main provisions of the d-trees method are analyzed. The examples that convince the high efficiency of the method of d-trees are given. The results are used in the design of amplifiers of special purpose.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.240
Teacher spread0.235 · 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
Published2019
Admission routes1
Has abstractyes

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