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Record W2614467585 · doi:10.1109/apec.2017.7931061

Power and frequency controllable multi-level MHz inverter with soft switching

2017· article· en· W2614467585 on OpenAlexaff
Hamed Tebianian, John E. Quaicoe, B. Jeyasurya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAmplifierElectrical engineeringInverterHarmonicsComputer scienceBandwidth (computing)Electronic engineeringRadio frequencyResonant inverterVoltageEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Wireless Power Transmission Systems (WPTSs) that use capacitive or inductive coupled resonators need to be designed at MHz frequencies to avoid large passive components. Some wireless power applications need power source with frequency control as well as output power control. The advent of high-frequency high-power enhancement-mode Gallium Nitride (eGaN) FETs has made the idea of using efficient switch-mode inverters, instead of linear Radio Frequency (RF) amplifiers for wireless power applications, more realizable. One of the major challenges for this replacement is the design of power and frequency controllable soft switching inverters at MHz ranges with low-pass output filter. This paper presents a soft switching multi-level inverter with dynamic dead-time control designed at 13.56 MHz with 4 MHz output frequency bandwidth. The inverter is designed to eliminate the 3rdand 5thharmonics in the output voltage, which removes the need for a high quality resonant output filter. It is shown through simulation that the inverter can operate as a voltage and frequency controllable supply and deliver almost constant 50W to the load with 4 MHz bandwidth.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.216
Teacher spread0.197 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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