Bibliographic record
Abstract
Many a mechanical application involves power transmission from a high-speed motor to a low-speed load. However, existing speed-reduction mechanisms are usually a major sink of energy and information in mechanical transmissions. Energy and positioning information are lost through: (a) friction between sliding components; (b) compliance; and (c) backlash. A novel transmission for speed reduction, Speed-o-Cam, is currently under research at McGill University's Centre for Intelligent Machines (CIM). The transmission is based on the layout of pure-rolling indexing cam mechanisms, and hence, eliminates backlash and friction. Besides zero backlash and low friction losses, Speed-o-Cam also offers the possibility of high stiffness, another essential attribute for high-accuracy applications. This thesis focuses on the aspects of both model development and mechanical-parameter identification of a spherical prototype of Speed-o-Cam. Our main interest lies in identifying the mechanism stiffness. In order to conduct experiments on the prototype, a testbed was designed and fabricated. A mathematical model of the testbed is first formulated. Based on this model and the results of experiments, the parameters of the Speed-o-Cam prototype are identified. In the process, the stiffness and damping parameters of the couplings of the testbed are also identified. Power efficiency is an important indicator of speed reducing mechanisms. For the Speed-o-Cam prototype, this indicator is also estimated experimentally.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".