Experimental and finite element analysis of an endoscopic tooth-like tactile sensor
Bibliographic record
Abstract
This paper reports on the finite element analysis and experimental study of a prototype PVDF endoscopic tooth-like tactile sensor capable of measuring compliance of a contact object. Present days endoscopic graspers are designed tooth-like in order to grasp slippery tissues. However they are not equipped with tactile sensors to measure the compliance of tissue. The tactile sensor consists of rigid and compliant cylindrical elements. Determination of the compliance of the sensed objects is based on the relative deformation of contact object/tissue on the compliant and rigid element of the sensor. The polyvinylidene fluoride (PVDF) film sandwiched between rigid cylinder and plate and also between the two base plates has been used to measure the force applied on the rigid element and the total force applied on the sensor, respectively. Using the finite element method, the rigid and compliant elements are modeled as solid and elastic foundations, respectively. The data obtained for the force variation are plotted for the various modulus of elasticity of the sensed object. An array of the sensors was also designed in two different configurations depending on the method of measuring the total force. In one configuration, the total force is measured on the sensed object using common base plates and in the other configuration it is measured using different base plates arrangement. It has been shown that good agreement exists between the finite element results and experimental values. The sensor exhibits high force sensitivity and good linearity. Further, an array of these sensors could be miniaturized to integrate with commercial endoscope.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".