Cluster Analysis Based Arc Detection in Pantograph-Catenary System
Why this work is in the frame
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Bibliographic record
Abstract
The pantograph-catenary system, which ensures the transmission of electrical energy, is a critical component of a high-speed electric multiple unit (EMU) train. The pantograph-catenary arc directly affects the power supply quality. The Chinese Railway High-speed (CRH) is equipped with a 6C system to obtain pantograph videos. However, it is difficult to automatically identify the arc image information from the vast amount of videos. This paper proposes an effective approach with which pantograph video can be separated into continuous frame-by-frame images. Because of the interference from the complex operating environment, it is unreasonable to directly use the arc parameters to detect the arc. An environmental segmentation algorithm is developed to eliminate the interference. Time series in the same environment is analyzed via cluster analysis technique (CAT) to find the abnormal points and simplified arc model to find arc events accurately. The proposed approach is tested with real pantograph video and performs well.
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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 | 0.001 | 0.002 |
| 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 it