Reactionless System Design through Decomposition and Integration Concept for Green Manufacturing
Bibliographic record
Abstract
When mechanisms move, because the position of the center of mass (CoM) is changing and also the angular momentum is changing, there is vibration within the system, this can degrade the accuracy performance when the system is used in space. Dynamic balance can be applied to address the above problem. Normally, dynamic balancing is accomplished through employing counter-masses or counter-rotations approach. The potential issue is that the more weight and inertia are included inside the system, which is not cost-effective. Here the authors suggest that one can accomplish dynamic balancing condition based on employing the naturally dynamically balanced mechanisms rather than resorting to the old counterweights approaches. For instance, one can accomplish the reactionless condition based on the reconfiguration concept. One does not employ counter-mass but via reconfiguring the system by shifting the linkage, which does not make the system get to be heavy and therefore, reduce the energy costs and achieve green manufacturing. On the basis of this concept, firstly dynamically balance a single limb based on the reconfiguration technique (decomposition) and then integrate the balanced limbs to construct the entire parallel manipulator (integration); i.e. the decomposition and integration concept. Finally, with the mechanical reconfiguration, the control laws governing the operation of the mechanism also need to be changed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".