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Record W4254727774 · doi:10.1109/ultsym.2017.8092099

Optimal phase on biaxial driven transducers based only on electrical power measurements

2017· article· en· W4254727774 on OpenAlexafffund
Sagid Delgado, Laura Curiel, Oleg Rubel, Geovane Da Silva, Samuel Pichardo

Bibliographic record

Venue2017 IEEE International Ultrasonics Symposium (IUS) · 2017
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsMcMaster UniversityThunder Bay Regional Research InstituteLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransducerMaterials scienceLead zirconate titanatePiezoelectricityAcousticsElectrodePhase (matter)ActuatorUltrasonic sensorOptoelectronicsElectrical engineeringComposite materialFerroelectricityDielectricPhysics

Abstract

fetched live from OpenAlex

The biaxial driving technique enhances the mechanical response of a piezoelectric actuator by dephasing two orthogonal electrical fields to the propagation and lateral electrodes. This paper presents results on the relationship between the electrical response and the phase at which the maximum acoustic efficiency was reached on a single element biaxially driven ultrasound transducer. Eight air-backed biaxial transducers (6.4 mm × 6.4 mm × 13.7 mm) fabricated with hard lead zirconate titanate were used for testing. Average of the resonant frequency on propagation and lateral modes for all transducers was 133 (±0.4) kHz and 138.1 (±0.5) kHz respectively. The effective acoustic power was measured using the radiation force method and the amount of power delivered to each electrode on the biaxial transducer was varied. The results of the analysis show that the optimal phase between electrodes can be obtained by analyzing the reflected power on the lateral electrode of the biaxially driven transducer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.327
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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