MétaCan
Menu
Back to cohort
Record W2029956323 · doi:10.1109/acc.2012.6315387

A speed regulating scheme for air-turbine dental handpieces

2012· article· en· W2029956323 on OpenAlexaff
Vahid Zakeri, Siamak Arzanpour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRam air turbineDuty cycleElectronic speed controlSolenoid valvePulse-width modulationController (irrigation)Computer scienceControl theory (sociology)PID controllerProcess (computing)TurbineAutomotive engineeringMechanical engineeringEngineeringTemperature controlVoltageControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

Air-turbine dental handpieces (ATDH) are used in most of dental practices as the main cutting tool. However, the speed of these handpieces is reduced while cutting a tooth; because no speed controller has been provided for them. This speed-reduction decreases the cutting efficiency of air-turbine handpieces. A review of literature indicated no study concerned with designing a speed controller for ATDH. Thus, in this paper, a control scheme is proposed for regulating the speed including `measurement', `actuation', and `controller-design' stages. An accelerometer was employed to capture vibrations of the handpiece; and the frequency associated with the first peak in the spectrum was indicated to represent the speed. An on/off solenoid valve with pulse-width modulation (PWM) technique was employed to regulate the speed through controlling the input pressure to the handpiece. Changing the duty-cycle of the PWM pulses could vary the speed. A process model from the applied duty-cycle to the measured speed was obtained. This process model includes a linear dynamic and a nonlinear static part. Then, a proportional-integral- (PI) controller was selected, and the gains were tuned. The practicality and efficiency of the proposed control scheme with the PI controller was confirmed by experimental results.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
GPT teacher head0.505
Teacher spread0.299 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicQuality and Safety in HealthcareFrench-language works237,207