MétaCan
Menu
Back to cohort
Record W109904125

A Dental Assisting System for Procedures Performed by Air–Turbine Handpieces

2013· dissertation· en· W109904125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typedissertation
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRam air turbineTurbineMechanical engineeringEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

The present thesis introduces a dental assisting system (DAS) for procedures that are performed by air–turbine dental handpieces. Dental restoration is a process that begins with removing carries and affected tissues to retain the functionality of tooth structures. Air–turbine dental handpieces are high–speed rotary cutting tools that are widely used by dentists during this operation. The next stage in the process is filling the cavity with appropriate restorative materials. “Amalgam” and “composite” are two dental restorative materials that are extensively used by dentists. Most old restorations eventually fail and need to be replaced. One of the difficulties in replacing failing restorations is discerning the boundary of the restorative materials. Dentists may remove healthy tooth structures while replacing tooth–colored composites. Although the visibility issue is less challenging for amalgam materials, replacing them still results in loss of healthy tooth layers. Developing an objective and sensor–based method is a promising approach to monitor restorative operations and prevent removal of healthy tooth structures. The designed DAS uses the audio signals of ATDH during the cutting process. Audio signals are rich sources of information and can be analysed to identify a particular zone of cutting. Support vector machine (SVM), a powerful algorithm for classification, is employed to differentiate the tooth structures from composite/amalgam samples based on their cutting sounds. The averaged short–time Fourier transform coefficients are selected as the features; and the performance of the SVM classifier is evaluated from different aspects such as number of features, feature scaling methods, and the utilized kernels. The obtained results indicated capability and efficiency of the proposed scheme. The developed DAS can also measure the speed of ATDH, and maintain it during loaded conditions. An indirect speed measurement method is introduced based on the vibration/sound of ATDH. This measurement technique is explained theoretically based on the rotating unbalance concept and the vibration of a fixed–free beam. To control the speed, a proportional–integral controller is designed and tested. The feasibility of this controller in maintaining the speed in the loaded conditions was confirmed by simulations and experiments.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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