Computer Implementation of the Dichotomy Algorithm for Transcendent Equations when Determining the Thread Tension
Bibliographic record
Abstract
The studies of the computer implementation of the dichotomy algorithm for determining the thread tension on technological equipment determined the values of the thread tension.It is proved that the number of guides on each specific technological machine, the radius of curvature of each guide, the angle of the thread of the guide, the angle of the radial coverage of the thread, the physic-mechanical and structural characteristics of the thread influence the magnitude of the tension of the threads.The value of the angles of the thread guides and the angles of radial coverage of the thread by the surface of the guide are determined by the geometric parameters and the design of both the filing system on the technological equipment and specific guides.Thanks to this, it became possible at the initial stage of the design of the technological process to determine the tension of the thread in front of the formation zone, depending on the geometric and structural parameters of the equipment and the physico-mechanical and structural characteristics of the thread.The increase in thread tension occurs due to friction in the contact area with the guides.The magnitude of the friction forces depends on the material of the thread and the guide, the ratio of their geometric dimensions (the radius of the cross section of the thread and the radius of curvature of the guide in the contact zone), the actual angle of the thread of the guide and the angle of the radial coverage of the thread on the surface of the guide, the physicomechanical and structural characteristics of the thread, tension threads in front of the guide.Sequential passage of the thread along the guides, from the entrance zone to the zone of fabric formation and knitwear, leads to a stepwise increase in tension.In this case, the output tension parameter after the previous guide will be an input parameter for the subsequent guide, which allows the use of recursion in determining the tension in front of the formation zone.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".