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Record W2553891183 · doi:10.1109/iccps.2016.7751087

Keynote Speech 1: Wireless power harvesting and transfer

2016· article· en· W2553891183 on OpenAlexaff
Zhi-Zhang David Chen, Wen‐Piao Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWireless power transferWirelessCommercializationElectrical engineeringMaximum power transfer theoremComputer scienceEnergy harvestingPower (physics)TelecommunicationsElectromagneticsEngineeringElectronic engineeringSystems engineeringPhysics

Abstract

fetched live from OpenAlex

Electromagnetics forms the foundation of modern electrical and electronic systems that see the technologies we enjoy and take for granted today. It has been used to transfer information and power in daily lives. In the wireless domain, however, it had not been considered and employed much for power transfer until 2008 when a MIT research team lead by Prof. Marin Soljacic successfully demonstrated the mid-range wireless power. Since then, many efforts and much progress have been made to improve power transfer efficiency, reduce system size and include more functionality; the goal is to cut the last wires in electrical and electronic devices and systems if all possible. In this talk, we will first present the principles of the wireless power harvesting and transfer and then focus on mid-range wireless power transfer and its applications and commercialization. We will present the challenges, progresses, and future directions in the areas.

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0960.057

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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
GenreOther

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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