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Design Method for Differentially-Driven Capacitive Wireless Power Transfer Systems

2020· article· en· W3116469805 on OpenAlexaff
Aaron Clements, Rowan McDonald, Masoud Ahmadi, Thomas Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCapacitive sensingWireless power transferCapacitanceMaximum power transfer theoremTransmitterTransmission lineElectronic engineeringResonatorPower (physics)Electrical engineeringComputer scienceTopology (electrical circuits)EngineeringPhysicsElectromagnetic coilChannel (broadcasting)

Abstract

fetched live from OpenAlex

A method of matching a capacitive power transfer system (CPT) is shown. A six-plate design driven by a balanced (differential) source is analyzed. The balanced design neutralizes several plate capacitances by imposing a virtual ground. The capacitance network linking the transmitter and receiver is simplified to an equivalent half circuit. A matching method is shown that reduces the network capacitances to two resonators coupled through a lumped element transmission line. The theory is described. Using this method, a CPT system was built for 13.56 MHz that delivers 230 W of load power at 80% efficiency and 950 W at 75% efficiency. Experimental results demonstrate how the differential source and load configuration neutralize a floating ground plate at the load.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.232
Teacher spread0.202 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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