Optimization of Bench Dancing Dragon Team Motion Based on Collision Detection Model and Numerical Simulation
Bibliographic record
Abstract
As an important folk cultural treasure, "Bench Dragon" attracts many audiences with its unique dance form [1]. Based on mathematical modeling and optimization algorithm, this paper discusses the dynamic marching process of the dragon dance team on the spiral line, focusing on the path planning and speed optimization of the coil in and out, aiming to improve the smoothness and safety of the dragon dance performance, so as to enhance the visual impact of its cultural heritage. Firstly, the position of the dragon head over time is deduced through the conversion of polar coordinates and Cartesian coordinates, and the positions of the dragon body and tail parts in each second are calculated step by step by using the geometrical characteristics of the dragon [2]. Meanwhile, the central difference method was applied to solve the velocity of the key nodes, and the model results were visually examined to verify the validity of the model [3]. Then, the position model and the collision detection model of the multi-bench system were constructed, the minimum distance between benches was calculated, and the collision was detected by the differential method, which finally led to the conclusion that the dragon's head collided with the 8th board of the dragon's body at about 412.6 seconds [4]. Finally, based on the previous research results, the collision detection model was constructed and the critical point was measured, and the range of the pitch to satisfy the condition was determined to be 0.450~0.451 meters using the global traversal method. The research in this paper provides theoretical support for the safety and fluidity of non-heritage dragon dance activities.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".