Extracting of Sagging Profile of Overhead Power Transmission Line Via Image Processing
Bibliographic record
Abstract
Sagging of the conductor in transmission line has a vital role in the safety, reliability and efficiency of power transmission. Transmission lines must be designed to guarantee the maximum static loading capacity. This is done by maintaining the minimum vertical clearance between the cables and the ground. However, the increase of the cable length between two tower, leads to the high cost of material and electrical energy loss, as well as increasing the possibility of intervention. On the other hand, reducing the line sagging induces high tension in the conductor, which may lead to damage of the conductor. To assure a safety sagging profile, an inspection is essential at the establishment and maintainance of the power transmission lines. In this paper firstly the mathematical formulation of long and heavy cables are developed. Then an image processing method is applied for inspection of cable sagging. To investigate the method a reconfigurable experimental setup is designed to provide various sagging profiles and the sagging profile is extracted via image processing and the result is compared to that of analytical method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".