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Record W4367359656 · doi:10.1109/tcsii.2023.3271421

A New Class of Nonlinear Resonance Networks Modeled by Levinson–Smith and Liénard Equations

2023· article· en· W4367359656 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2023
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRLC circuitNonlinear systemResistorResonance (particle physics)Control theory (sociology)Nonlinear resonanceRC circuitElectronic circuitFilter (signal processing)Differential equationSeries (stratigraphy)LC circuitVariable (mathematics)Topology (electrical circuits)MathematicsVoltageMathematical analysisPhysicsComputer scienceCapacitorQuantum mechanics

Abstract

fetched live from OpenAlex

We propose a new class of nonlinear resonance networks which are modeled by standard second-order nonlinear differential equations of the Levinson-Smith type or its subset, the Liénard type. In particular, we modify series and parallel RLC resonance networks such that the passive resistor in these networks is composed of a fixed part and a variable voltage- or current-controlled part. The variable resistor is then made controllable by one of the circuit’s state-space variables leading to an embedded self feedback control mechanism. A possible discrete component circuit realizing the proposed concept is presented along with its simulations. The filter response behavior of one of the modified series resonance circuits is also experimentally verified using a Field Programmable Analog Array.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.231
Teacher spread0.213 · 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