Study on Improving Transmission Line Hazard Monitoring Using AdaBoost Algorithm in Multi-dimensional Data Environment
Bibliographic record
Abstract
During the operation of transmission lines, there are sudden failures and a large number of slowdeveloping, preventable "gradual" failures, which have seriously threatened the safe and stable operation of the transmission system.Based on analyzing the multidimensional environmental factors affecting line safety, the study proposes a method for identifying the operating state of transmission lines based on the AdaBoost integrated learning algorithm, and develops a set of transmission line hidden danger monitoring system.A decision pile based on Ginin indicators is used as a weak classifier, and the hidden danger monitoring results and their confidence levels are output by training and weighted summation of multiple weak classifiers.Using historical data for validation experiments, the proposed method achieves an accuracy of 95.92% in recognizing the operating state of transmission lines, which is a more superior performance compared with traditional machine learning methods.The system can basically realize the hidden danger monitoring of transmission lines, so as to assist the field operation and maintenance personnel of transmission lines to carry out fault investigation, and reduce the transmission line tripping due to the development of hidden danger into fault.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".