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Record W2989898555 · doi:10.22215/etd/2019-13438

A Dynamic Solution to Spatial Misalignment in Strongly Coupled Magnetic Resonance Wireless Power Transfer Systems

2019· dissertation· en· W2989898555 on OpenAlexaff
Paulyn J. S. Mulles

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsImpedance matchingMaximum power transfer theoremAmplifierTransmitterInput impedanceWireless power transferElectrical impedanceElectronic engineeringCoupling (piping)Power (physics)EngineeringElectrical engineeringRF power amplifierComputer scienceElectromagnetic coilPhysicsCMOSChannel (broadcasting)

Abstract

fetched live from OpenAlex

Spatial misalignment is the leading cause of power transfer efficiency loss in a wireless power system; the misalignment causes the magnetic coupling of the system to change, resulting in a non-optimal load impedance. This research work presents the design and evaluation of an adaptive, near-field transmitter that can maintain the system efficiency when spatial misalignment occurs. The design consists of a power amplifier, a directional coupler, a tuneable impedance matching network, a gain-phase detector, and a microcontroller. An algorithm was created to determine the varying load impedance; another modified the configuration of the tuneable matching network for one that can match to the new load impedance. The developed class EF 2 power amplifier delivered 7.5 W of power to the load, with a drain efficiency of 82.4%, at 13.56 MHz. The adaptive transmitter prototype maintained an average of 55% system efficiency for a separation distance of 1.5 cm to 3 cm.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.205
Teacher spread0.200 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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