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Record W2586654079 · doi:10.1109/tencon.2016.7848105

Current-fed full-bridge and half-bridge topologies with CCL transmitter and LC receiver tanks for wireless inductive power transfer application

2016· article· en· W2586654079 on OpenAlexaff
Suvendu Samanta, Akshay Kumar Rathore, Sanjib Kumar Sahoo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaximum power transfer theoremNetwork topologyTopology (electrical circuits)Wireless power transferTransmitterBridge (graph theory)Electrical engineeringInverterPower (physics)VoltageElectronic engineeringEngineeringWireless sensor networkComputer scienceElectromagnetic coilComputer networkChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

This paper presents a performance comparison between inductive power transfer (IPT) with current fed full bridge and half bridge converter topologies. Generally current-fed IPT topology with parallel LC tank is not suitable for medium power application due to higher voltage stress on inverter switches. In this comparison a modified CCL tank network at transmitter is selected for both the half and full bridge topologies. Mathematical analysis of both converter is presented to perform the comparison. The full bridge topology provides slightly better efficiency compare with half bridge whereas the overall component cost for full bridge is higher due to higher component count. To verify the analysis numerical simulation is performed in PowerSIM 10 for 2.0kW power transfer.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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 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

Citations16
Published2016
Admission routes1
Has abstractyes

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