Bibliographic record
Abstract
Badminton is a competitive sport with high real-time requirements. To build a badminton robot and enable human-robot sparring, real-time dynamic tracking of fast-moving badminton trajectories is essential. The tracking problem of dynamic targets is widespread in various fields, including industrial production, daily life, and military applications. Existing badminton trajectory planning algorithms face challenges in accurately tracking moving targets and evaluating the stability of badminton flight paths. To enhance the badminton trajectory planning capability, this paper presents a neural network-based algorithm for badminton trajectory prediction. Firstly, a badminton aerodynamic model is established based on the flight characteristics of a badminton shuttlecock. Then, the motion trajectory planning constraint parameters are analyzed for the parameters involved in the dynamic model. The neural network is introduced to predict the badminton trajectory and facilitate accurate tracking of the badminton path. Experimental results demonstrate that the proposed method can effectively track the dynamic path of a badminton shuttlecock in real-time, reduce the deviation of drop-off distances, and improve the accuracy of drop-off prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".