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Record W2896130158 · doi:10.1088/1367-2630/aae3fb

Phase cascade lattice rectifier array: an exactly solvable nonlinear network circuit

2018· article· en· W2896130158 on OpenAlexaff
Mohammad Hassan Nazari, J Chen, A.M. Gole, Mineui Hong, Parimita Mohanty, Shyamsunder Erramilli, Onuttom Narayan

Bibliographic record

VenueNew Journal of Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhysicsCascadeNonlinear systemLattice (music)Rectifier (neural networks)Topology (electrical circuits)Phase (matter)Quantum mechanicsElectrical engineeringArtificial neural network

Abstract

fetched live from OpenAlex

An exact analysis of a 2-D lattice network consisting of N × N sites with rectifier and AC source elements with controllable phases reveals a method for generating ripple-free DC power without the use of any filtering circuit elements. A phase cascade configuration is described in which the current ripple in a load resistor goes to zero in the large N limit, enhancing the rectification efficiency without requiring any additional capacitor or inductor based filters. The integrated modular configuration is qualitatively different from conventional rectenna arrays in which the source, rectifier and filter systems are physically disjoint. Nonlinear networks in the large N limit of source-rectifier arrays are potentially of interest to a fast evolving field of distributed power networks.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

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