Efficient unmanned aerial vehicle paths design for post‐disaster damage assessment of overhead transmission lines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it