Increasing the Cost-Effectiveness of AC Interference Mitigation Designs with Integrated Electromagnetic Field Modeling
Bibliographic record
Abstract
Abstract The electricity transmission, gas transmission, and railway industries have developed various methods for the calculation of voltages induced in infrastructure following power line corridors. These tools are typically based on simplified topologies and assumptions that make the implementation of the required algorithms more tractable and data entry screens more alluring. On the other hand, such an approach suffers from reduced accuracy, due to the required simplifying assumptions, and furthermore excludes the possibility of studying complex systems for which the designer is unable to determine what simplifying approximations are the most appropriate. In the absence of confidence in his or her calculations, the designer tends to be overly conservative, resulting in excessive mitigation. This situation arises in particular when the system includes buried components whose through-earth coupling interactions are significant. This paper illustrates this point with a case study and parametric analysis, showing how a calculation based on integrated electromagnetic field modeling results in a more accurate assessment of interference levels and therefore more suitable mitigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".