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A Comparative Study between Different Loop Antennas Topologies for Wireless Power Transmission based on Modal Analysis

2021· article· en· W3161146943 on OpenAlexaff
Ferdaous Abderrazak, Eva Antonino‐Daviu, Miguel Ferrando‐Bataller, Larbi Talbi, Ali Al Qaraghuli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsWireless power transferMaximum power transfer theoremNetwork topologyTransmitterTransmission (telecommunications)Electronic engineeringWirelessPower transmissionElectrical engineeringTopology (electrical circuits)Computer sciencePower (physics)EngineeringTelecommunicationsPhysicsComputer networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

Wireless power transfer (WPT) using magnetic coupling between loop antennas is an emerging technology, which could solve difficulties and hazardous technical problems of wired power transmission. From several millimeters to several hundred millimeters, WPT technology has reached kilowatts power level with high grid load of efficiency, which has brought significant benefits to medical applications, automation systems, consumer electronics, etc. Yet, WPT systems are extremely sensitive to the alignment between the transmitting and the receiving coils. A dual transmitter topology has been proposed to overcome the misalignment problems and maintain high power transfer efficiency. Nevertheless, the misalignment sensitivity between the transmitters was not considered. A comparative study based on the Theory of Characteristic Modes between three different topologies of the transmitting systems in MHz level is conducted in this paper. To enhance the comparison, the power transfer efficiencies of the three models are calculated regarding the same positions of the receiver.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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