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Record W2996575228 · doi:10.1109/iecon.2019.8927761

A Novel Three Leg Inverter for High Power Hybrid Inductive and Capacitive Wireless Power Transfer System

2019· article· en· W2996575228 on OpenAlexaff
Deepa Vincent, Phuoc Sang Huynh, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWireless power transferMaximum power transfer theoremTopology (electrical circuits)InverterCapacitive sensingElectrical engineeringPower (physics)Compensation (psychology)Computer scienceCapacitorElectronic engineeringWirelessEngineeringPhysicsVoltageTelecommunications

Abstract

fetched live from OpenAlex

A novel topology for hybrid wireless power transfer (HPT), which uses both capacitive and inductive action, for electric vehicle charging is proposed in this paper. A three leg inverter topology is used for HPT. Two legs of the inverter, switched at 85 kHz, are used for inductive wireless power transfer (IPT) and the third leg, switched at 1 MHz, is used for capacitive wireless power transfer (CPT). Mixed switching frequency operation of the converter, is the novelty in proposed topology. Series-series compensation is used for IPT and dual LC compensation is used in CPT. The effectiveness of the proposed topology is simulated for a power level of 1 kW. IPT contributes to twice the power transfer than that of CPT.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.173
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations7
Published2019
Admission routes1
Has abstractyes

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