Identification of Icing Thickness Based on the On-line Monitoring of Insulators
Bibliographic record
Abstract
In order to obtain the icing characteristic parameters in time, the on-line monitoring method was used to extract the features of insulators for prevent the icing disaster. The ice thickness of insulators is identified by the monitoring device ZHY810C according to the edge detection algorithm of Canny operator, which is significant for the ice disaster prevention in extreme environment. The calculation model of icing thickness is established by digital image processing technology and intelligent algorithm. The Gaussian low-pass filtering radius is conducted to reduce image noise and to sharpen image edge and then the gradient of grayscale is calculated. After that, the non-maximum suppression threshold and double threshold algorithm are chosen to detect the optimal edge point from the icing image. Finally, the icing image can be thinned based on the region growth method. Hence, the icing thickness of insulators can be monitored in real-time by the online monitoring equipment ZHY810C and calculated by Canny operator edge detection algorithm.
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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".