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A Wireless Power Transfer System with Full-Duplex Data Communication for 24V DC Industrial Proximity Sensor

2023· article· en· W4390416119 on OpenAlexafffund
Matthew Schraa, Sanjida Moury, Farhan A. Ghaffar, Cameron Scott Ball, Jonathan Letwin, Jack Birkett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsLakehead UniversityBarrie Urology Group
FundersYork University
KeywordsFrequency-shift keyingKeyingWireless power transferElectrical engineeringDuty cycleWirelessEngineeringMaximum power transfer theoremTransfer (computing)Electronic engineeringChannel (broadcasting)Computer sciencePower (physics)TelecommunicationsVoltagePhysicsDemodulation

Abstract

fetched live from OpenAlex

A high-frequency H-bridge LLC wireless power transfer (WPT) with full-duplex data transfer is presented in this paper. The data transfer from the source to the sensor side is achieved using Frequency Shift Keying (FSK). In contrast, Load Shift Keying (LSK) is used to transfer data from the sensor to the source (or the user). The proposed WPT utilized an LLC converter to achieve soft switching. Moreover, two control parameters (frequency and duty ratio) of H-bridge LLC give the freedom of using one of them (frequency) for data transfer and the other (duty ratio) to regulate the output voltage, which eliminates the requirement for a separate resonant tank for FSK. The detailed simulation results for the Industrial Proximity sensor (24V and 360mW), as well as the experimental results are shown to demonstrate the performance and features of the presented work.

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 score0.002
Threshold uncertainty score0.007

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 science0.0010.000
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 head0.061
GPT teacher head0.245
Teacher spread0.183 · 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".

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Citations1
Published2023
Admission routes2
Has abstractyes

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