New Methodology for Train Component Crash Dynamic Response by High-Speed Photography 3D Analysis Method
Bibliographic record
Abstract
High-speed photography is an indispensable part of train component crash tests. It is used to analyze sequence images of a train component crash, and the accuracy of the analysis for data is essential for later research. The aim of this study is to propose a new methodology - the high-speed photography 3-dimensional (3D) analysis method - which can be used to investigate the train component crash dynamic response. The 3D analysis method requires images of the same object taken synchronously from multiple directions/cameras. The images" sequence of each direction is calculated to 2D data that are reconstructed to 3D data for analysis. Compared with the real result, the result of a high-speed photography image sequence 3D analysis has a difference deformation of 1.91%. Compared with the result of a ground velocity testing system, the result has a 1.17% difference in initial crash velocity. Compared with an acceleration sensor, acceleration changes are basically identical in the crash process. The high-speed photography sequence image motion 3D analysis method, compared with the 2D analysis method, which is more accurate and real, makes up for various errors and defects.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.005 | 0.002 |
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".