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Record W1905460191 · doi:10.1002/cta.2024

Design and optimization of a 3‐coil resonance‐based wireless power transfer system for biomedical implants

2014· article· en· W1905460191 on OpenAlexaff
Ying Yi, U. Büttner, Yiqiang Fan, Ian G. Foulds

Bibliographic record

VenueInternational Journal of Circuit Theory and Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsElectromagnetic coilWireless power transferInductanceCoil tapMaximum power transfer theoremResonant inductive couplingResonance (particle physics)Q factorInductorElectrical engineeringPower (physics)RADIUSCapacitorWirelessCoil noiseElectronic engineeringMaterials scienceEngineeringComputer sciencePhysicsTelecommunicationsEnergy transferRogowski coilResonator

Abstract

fetched live from OpenAlex

Summary This paper presents a resonance‐based wireless power transfer system using a single layer of inductor coil windings, in a pancake configuration, in order to obtain a compact system for implantable electronic applications. We theoretically analyzed the system and characterized it by measuring its inductance, self‐resonant frequency, and quality factorQ. In our resonance‐based wireless power transfer prototype, we proposed a 3‐coil system, using two 15‐mm radius implantable coils, with a resonance frequency of 6.76 MHz. This system can effectively transfer power for a distance of up to 50 mm. Moreover, our proposed 3‐coil system can achieve a highQ‐factor and has a comparable power transfer efficiency (PTE) to previously reported works about 3‐coil and 4‐coil systems. The experimental PTE can achieve 82.4% at a separation distance of 20 mm and more than 10% PTE at a distance of 40 mm. Copyright © 2014 John Wiley & Sons, Ltd.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations57
Published2014
Admission routes1
Has abstractyes

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