Bibliographic record
Abstract
A two-straight-legged walking mechanism with flat feet is designed and built to study the passive dynamic gait. It is shown that the mechanism having flat feet can exhibit passive dynamic walking as those with curved feet, but the walking efficiency is significantly lower. It is also shown that the balancing mass and its orientation are effective for controlling side-to-side rocking and yaw, which have significant effects on steady walking. The effects of various parameters on the gait patterns are also studied. lt is shown that changes in the ramp angle have the most dominant effect on the gait pattern as compared with the changes in the hip mass, ramp surface friction and size of the flat feet. More specifically, as the ramp angle increases, the step length increases while the range of the side-to side rocking angle decreases and the step length dictates the walking speed and the gravitational power. Another finding, is that adding a hip mass improves the walking efficiency by allowing the mechanism to walk on a flatter ramp. This research enables us to gain a better understanding of the mechanics of walking. Such an understanding will have a direct impact on better design of prostheses and on the active control aspects of bipedal robots.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".