A Novel Regression Model-Based Toolbox for Induced Voltage Prediction on Rail Tracks Due to AC Electromagnetic Interference of Adjacent Power Lines
Bibliographic record
Abstract
AC electromagnetic interference between rail tracks and adjacent power lines causes serious concerns about personnel and railway equipment safety. The existing AC interference analysis method uses the complex computer simulation software to estimate induced voltages on rail tracks, and such simulation becomes especially difficult at the transmission line routing stage when only limited information is available. To overcome this challenge, a novel regression model-based toolbox is developed in this paper to predict induced voltages on rail tracks due to AC interference. To develop this toolbox, the dataset acquisition is a critical step due to very limited research conducted in this area. A dataset is produced in this study using our newly developed AC interference analysis method, where variations of various factors are considered, including the power line's current, the separation distance between power lines and railway, the ballast resistance, and the length of rail tracks. To improve the accuracy, hyperparameters of regression algorithms are optimized by Bayesian optimization. Two models are eventually chosen to predict induced voltages on rail tracks: “Gaussian process regression” with “matern 3/2” kernel function; and a tri-layered “neural network” model with “sigmoid” activation function. The toolbox is accurate and easy-to-use for design engineers working on transmission line routing, and has been currently in use by Manitoba Hydro in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".