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Record W2973926072 · doi:10.1109/access.2019.2942562

A Ku-Band Microwave Wireless Energy Transmission System Based on Rectifier Diode

2019· article· en· W2973926072 on OpenAlexaff
Biao Hu, Yihong Zhou, Hao Li, Haiyang Wang, Tianming Li, Mohamed Helaoui, Fadhel M. Ghannouchi

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsRectennaRectifier (neural networks)Microwave transmissionElectrical engineeringAntenna (radio)MicrowavePIN diodeWirelessAperture (computer memory)DiodeComputer sciencePower transmissionKu bandElectronic engineeringPower (physics)EngineeringTelecommunicationsPhysicsAcousticsVoltage

Abstract

fetched live from OpenAlex

This paper demonstrates a Ku-band medium and long distance microwave wireless energy transmission system based on the rectifier diode. The transmitting compact Cassegrain antenna with an aperture of 1.5m forms a more centralized and uniform 2m x 2m field zone, which has more than 80% transmission efficiency. According to the distribution of receiving field, the multiple ways synthesizers connected to rectangular patch arrays are designed. And the results predict that the receiving efficiency of the antenna is directly related to the performance of the system, which reaches 50%, not including the possible insert loss. Besides, the Ku-band rectifying circuit is optimized and measured, and the comparing results show that the practical rectifier diode can obtain more than 40% conversion efficiency at the operating frequency of 12.75GHz. Finally, the microwave wireless energy transmission system is designed, and it finally obtains 4% DC-DC conversion efficiency at a distance of 60m, which is likely to be used in unmanned aerial vehicle hovering charging system, robot charging while working, isolated island wireless power supplying and wireless charging of various sensors.

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 categoriesMeta-epidemiology (narrow)
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 score0.290
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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.

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

Citations14
Published2019
Admission routes1
Has abstractyes

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