The maximum output force controller and its application to a virtual surgery system
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.
Bibliographic record
Abstract
It is difficult to achieve ideal virtual surgery transparency and stability when virtual tissue stiffness and damping are high. Typically, the stability of the surgery system is improved, while its transparency is sacrificed. In order to achieve high transparency in virtual surgical interactions, a maximum output force controller based on passive theory is proposed in this work. This controller is then applied in a virtual surgery system. The maximum output force controller predicts the maximum allowable output force above which the system passivity is broken and limits the force presented to the operator to this amount. The main contributions of this work include the following two parts: firstly, the maximum output force controller is developed and applied to a virtual surgery system; secondly, a new criterion for transparency is presented and analyzed for the level of transparency that can be achieved for a virtual surgical system when the stability is guaranteed. Experimental results show that the maximum output force controller can guarantee stability of the virtual surgical interaction with maximum transparency even when the virtual tissue stiffness and damping are high. In addition, the maximum output force controller is a self-adaptive controller. It works well without modification, regardless of the virtual tissue stiffness and damping.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it