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Record W2983221178 · doi:10.1109/ssd.2019.8893177

Investigation of the inductive link parameter variations on the wireless power transfer system efficiency

2019· article· en· W2983221178 on OpenAlexaff
Yosra Ben Fadhel, F. Doub, K. Yeferni, S. Rahmani, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsWireless power transferMaximum power transfer theoremTransmitterElectromagnetic coilElectrical engineeringBattery (electricity)Inductive couplingWirelessComputer scienceTransfer (computing)Electronic engineeringPower (physics)VoltageCoupling (piping)EngineeringTelecommunicationsChannel (broadcasting)PhysicsMechanical engineering

Abstract

fetched live from OpenAlex

In our days, all portable electronic devices require energy to operate properly. Batteries' short lifetimes that supply these devices are a major disadvantage for their good use. The problem becomes even more delicate when it comes to medical implants, for surgical operations necessity to replace the battery after each life cycle. Therefore, Wireless Power Transfer (WPT) has been emerged as a promising solution to overcome any health problem or any dangerous side effect caused by surgery. In this work, we have designed and implemented two WPT systems using the Resonant Inductive Coupling (RIC) technique to ensure WPT. Each system contains a transmitter and a receiver circuit. The difference is that we propose to vary several inductive link parameters from one system to another like the supply voltage, the coils design and the transfer distance between coils. The objective behind these experiences is to investigate the influence of the inductive link parameter variations on the power transfer efficiency. Experimental results have highlighted that inductive link parameter variations largely influence the power transfer efficiency between the transmitter and the receiver coil.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.176
Teacher spread0.165 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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