An Efficient Finite-Element Modelling Tool for Surgical Simulation: The ε-Mesh Radius
Bibliographic record
Abstract
Abstract This paper presents an algorithm for determining the boundary volume of local mesh for haptic rendering. Here haptic rendering is defined as generation of reaction forces and contact shape information for graphical display. Haptic rendering consists of a number of components: a) scene representation b) scene modelling for global collision detection c) collision detection; d) contact profile and e) scene display. As oppose to classical Finite Element Method or Multi-Body Systems dynamics, haptic rendering deals with the modelling of object where the user can interact with them in the virtual environment. This paper concerns with some aspects related to the interaction between the surgeon tool (probe or micro-robot) and a model of human tissue and organ. Specifically the paper addresses analogies between traditional notion of mesh generation used in finite-element analysis and notion of haptic rendering. In fact, when the user interacts with the object, it is not clear which level of accuracy is needed for the creating the sense of reaction forces at the point of interaction. That is the motivation for proposing the notion of ε-mesh in analogy with the similar notion used in the theory of adaptive meshing. The ε-mesh radius is a scalar quantity defined as a function of the material properties of the contacting bodies, the expected local geometry of the bodies, the direction and relative approach velocities of the bodies. Relationships are presented which show how relative coarseness of the ε-mesh radius with respect to the mesh sizes outside of the radius affect the relative reaction forces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".