Standard-wheel-based field calibration method for railway wheelset diameter online measuring system
Bibliographic record
Abstract
Laser displacement sensor (LDS)-based online measuring of the wheel diameter has been widely adopted in engineering for advantages such as noncontact, high efficiency, and high precision. For almost all these online measuring systems, calibration is certainly needed in order to obtain the extrinsic parameters of sensors. A field-based easy-to-operate, economical, and efficient calibration method is proposed for an LDS-based wheel diameter online measuring system. Only one standard wheelset is used to build the 3D calibration target that is also the measurement target of the system. The extrinsic parameters for each LDS are obtained through minimizing the residual summation of squares. A multistart framework combining the generation of certain numbers of uniformly distributed starting points and a nonlinear programming solver is adopted to solve the minimizing function to obtain the global optimizer. Factors include the number of standard wheelset placement and sensor noises that will result in calibration error are analyzed. Field experiments are carried out, and the correctness of the calibration method is verified through comparisons with manual caliper-measuring results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".