Configuration design and performance analysis of a multidimensional acceleration sensor based on 3RRPRR decoupling parallel mechanism
Bibliographic record
Abstract
There has been a growing interest of parallel mechanisms which offer a high degree of precision, stiffness and dexterity to name just a few of their benefits as opposed to their traditional serial counterparts. For the applications of parallel mechanisms in the field of sensor, previous work was focused on investigating the 6 degree-of-freedom force/torque sensors based on common Gough-Stewart platform. In this paper, a novel design of a multidimensional acceleration sensor is proposed based on 3RRPRR fully decoupling parallel elastic mechanism for kinetic information acquisition, specifically for measuring human motions. The translational elements of three perpendicular legs are served as elastic body which is manufactured by aluminum alloy. The detailed design of the structure is first introduced, followed by the mathematics modeling and performance evaluation. Finally, the finite-element analysis of resultant stress, strain and deformations is conducted based upon different input conditions to show the reliability of the proposed structure.
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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".