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Record W2608625855 · doi:10.1109/lmwc.2017.2690881

A Magnetic Tank System for Wireless Power Transfer

2017· article· en· W2608625855 on OpenAlexafffund
Zhu Liu, Zhizhang Chen, Jinyan Li

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsDalhousie University
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsWireless power transferTransmitterElectromagnetic coilPower (physics)Electrical engineeringPerpendicularWirelessMaximum power transfer theoremUnderwaterEngineeringAcousticsElectronic engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This letter presents a tank system for wireless power transfer with magnetically coupled resonance. It contains two interconnected transmitting resonant coils placed perpendicular to each other and forms a magnetic resonant tank. Due to the combined effects of these two transmitting coils, there will always be magnetic flux that goes through the receiver regardless of axial misalignment between the transmitter and the receiver. The transfer efficiency thus improves and becomes less sensitive to axial misalignment than the conventional system. A prototype is fabricated on FR4 printed circuit boards and tested. Both the simulation and test results show that the proposed system can enhance power transfer performances over a relatively good distance and a large range of misalignment angles. The receiver is functional even when it is perpendicular to the transmitter. Therefore, the proposed system can serve as a good candidate for applications such as wireless power supply to underwater motors.

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 score0.005
Threshold uncertainty score0.018

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)0.0050.003

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 head0.014
GPT teacher head0.202
Teacher spread0.189 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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