A Dental Assisting System for Procedures Performed by Air–Turbine Handpieces
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".