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Record W4401593195 · doi:10.1049/wss2.12089

High‐power radio frequency wireless energy transfer system: Comprehensive survey on design challenges

2024· article· en· W4401593195 on OpenAlexaff
Javad Soleimani, Güneş Karabulut Kurt

Bibliographic record

VenueIET Wireless Sensor Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWirelessWireless power transferEnergy transferRadio frequencyComputer scienceTelecommunicationsPower (physics)Electrical engineeringEngineeringPhysicsEngineering physics

Abstract

fetched live from OpenAlex

Abstract Feeding electrical components without having a physical contact was always a goal in electrical engineering. Nowadays, Wireless Power Transfer (WPT) is becoming the main way to provide energy for wireless sensors. WPT can be categorised into two primary techniques: radiative and non‐radiative methods. The authors uniquely delve into the utilisation of radiative methods, precisely the Radio Frequency (RF)‐WPT method. The authors focus on the factors and considerations for designing this kind of systems highlighting the specific nuances and challenges associated with high power wireless energy transfer systems and will try to define an efficient design method. A comprehensive survey is offered encompassing the entire system. It explores both transmitter and receiver systems, dissecting their subsystems and elements and challenges related to high power application one by one, while also elucidating the essential principles and integration factors.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.215
Teacher spread0.182 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

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