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Record W4232195194 · doi:10.32920/ryerson.14661039.v1

Magnetically coupled resonant wireless power transmission system that meets the Rezence efficiency and frequency specification

2021· preprint· en· W4232195194 on OpenAlexaff
Jean-Claude Leslie Clarke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWireless power transferResonatorElectronic engineeringElectromagnetic coilSpiral (railway)Electrical efficiencyCoupling (piping)Design flowPower (physics)AmplifierCapacitive sensingCapacitive couplingInductive couplingComputer scienceElectrical engineeringEngineeringCMOSPhysicsVoltageMechanical engineering

Abstract

fetched live from OpenAlex

High efficiency Class-E Power Amplifiers (PA) are difficult to analytically design using the original design equations. We present a high frequency (HF) Class-E PA design methodology that simplifies design in this thesis. A high-efficiency Class-E PA was designed using a low-cost power FET by following this design-flow. Due to their small size, it’s difficult to design efficient MCR-WPT resonators for portable electronics. We propose a novel multi-layer MCR-WPT Printed Spiral Coil (PSC) design and design methodology. Two MCR-WPT PSC resonators were designed for smartphones and tablets to meet the Rezence Self-Resonant Frequency and efficiency specifications using this novel design and design methodology. The MCR-WPT resonators power transfer efficiency is reduced when their separation distance is below the optimal Critical Coupling Distance (CCD) due to frequency splitting. We present a novel maximum-peak detection and auto-tuning circuit that automatically improves efficiency using capacitive tuning when the separation distance is below the CCD.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.196
Teacher spread0.185 · 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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

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