On statistical models of the amplitude and the duration of pulsed electromagnetic interference in automatic-control and telemechanic channels of subway lines
Bibliographic record
Abstract
On the basis of the results of experimental studies of the interference from the traction current of subway trains, the distribution type of this interference has been established. Approximation of experimental data by an analytical expression based on the Pearson curve family underlies the proposed method. The approximation is performed by calculating the first four distribution moments. The common array of the initial data on the electromagnetic interference is divided into two parts, depending on the type of the rolling stock—either with the switched-on-off tracking regenerative-rheostatic braking system or without it. The 7‒10-min recordings of the data on the traction current for trains with or without regenerative braking have been processed. To normalize the experimental data, the mathematical expectation estimate is subtracted from the former and the obtained values are divided by the estimate of the standard deviation. The resulting new data array has normalized properties, i.e., the zero mathematical expectation and a unit standard deviation. Then, from the normalized data array, the first four sampling moments are found, on the basis of which the corresponding Pearson curve is selected. The method underlies the estimation of the distribution density of the pulsed interference amplitude and the duration of the electromagnetic interference pulses. It has been shown that, in all 12 frequency ranges from 50 to 780 Hz that are used in subway automatic control and telemechanical devices, the distribution density of the pulsed interference amplitude and the interference pulse duration can be described by the analytical expression of the β-distribution of the first and second kind.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".