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Capacitive Resonant System to Charge Devices with Metallic Embodiments

2019· article· en· W3016210465 on OpenAlexaff
Susanna Vital de Campos de Freitas, Fabiano Cezar Domingos, Rashid Mirzavand, Pedram Mousavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitive sensingTransmitterWireless power transferTransposeElectrical engineeringPower (physics)WirelessComputer scienceElectronic engineeringMaximum power transfer theoremDegrees of freedom (physics and chemistry)Key (lock)EngineeringTelecommunicationsPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Even though wireless power transfer (WPT) is of current interest to researchers and industry, it still strives to become widely accepted. The main reason for this problem is the fact that WPT techniques suffer from alignment constraints. Additionally, they are either not able to transpose a metallic structure, or require multiple pairs of metallic plates aligned. This paper introduces a combination of inductive and capacitive power transfers in order to efficiently allow the charging of a receiver with a metallic embodiment. Furthermore, this new structure allows freedom of positioning the receiver on top of the transmitter. Simulations are performed to infer the system's efficiency versus frequency. Finally, it is demonstrated that the proposed technology can achieve an S21 up to -0.87 dB at 6.78 MHz.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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