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Record W2013843196 · doi:10.1109/58.852087

Class D amplifier for a power piezoelectric load

2000· article· en· W2013843196 on OpenAlexaff
Kodjo Agbossou, Jeremy Dion, S. Carignan, Mohamed Naceur Abdelkrim, A. Chériti

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2000
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsInnovation and Economic Development Trois RivièresUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHarmonicsAmplifierElectrical engineeringCapacitive sensingPiezoelectricityInverterAudio power amplifierPower (physics)Electronic engineeringPMUTEngineeringAcousticsMaterials sciencePhysicsVoltageCMOS

Abstract

fetched live from OpenAlex

We present a high efficiency inverter (>90%) that can drive an acoustic cavitation reactor with a 2 kW power between 10 and 100 kHz. This reactor is composed of numerous piezoelectric transducers and is particularly used to accelerate various industrial chemical reactions and destroy a variety of organic contaminants in water. The class-D amplifier or inverter is composed of power MOSFETs, type IRFP460, in a full bridge configuration driven by IR2110 circuits in bootstrap mode. The specific nature of the problem comes from the fact that, at frequencies slightly different from a resonant frequency frn, the load is mostly capacitive. The insertion of an appropriate low-pass filter in front of the load allowed an efficient solution to the problem due to the load being capacitive for harmonics. The realized system can provide nearly 2 kW to this type of piezoelectric load, with an efficiency of more than 95%

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 score0.023
Threshold uncertainty score0.077

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0230.007

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 head0.195
Teacher spread0.188 · 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

Citations86
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlSame topicWireless Power Transfer SystemsFrench-language works237,207