Efficient unmanned aerial vehicle paths design for post‐disaster damage assessment of overhead transmission lines
Bibliographic record
Abstract
Abstract The widespread distribution of overhead transmission lines increases the vulnerability of power grids to failures. Thus, power lines need to be timely inspected, especially before or during emergency‐related situations to ensure stable operation of the power grid. Traditional methods of visual inspection (satellites and helicopters) are inconvenient, often cannot be deployed and if they are deployed present a slow response time and high cost, which is very critical for fast post‐disaster damage identification. On the other hand, employing an unmanned aerial vehicle (UAV) offers a more efficient, reliable, and faster means for the assessment process. This article proposes a novel approach for the post‐disaster UAV‐based damage assessment of overhead power lines. In the proposed approach, the UAVs paths over the most critical loads are formulated as an optimisation problem with the objective of minimising the total inspection time while considering the recharging of the UAVs' batteries. To solve the problem, an efficient framework that optimises the UAVs flight paths is proposed to inspect the critical loads in an efficient order, while accounting for the UAV recharging. This guarantees that the UAVs complete the assessment tasks unlike existing benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".