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Record W3214223160 · doi:10.1109/access.2021.3125572

Evaluation and Mitigation of Electromagnetic Interference Between Railways and Nearby Power Lines: A Review

2021· review· en· W3214223160 on OpenAlexafffund
Chenyang Wang, Xiaodong Liang, Emerson Adajar

Bibliographic record

VenueIEEE Access · 2021
Typereview
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsManitoba HydroUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectromagnetic interferenceInterference (communication)Power (physics)Electromagnetic compatibilityElectrical engineeringTelecommunicationsComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

A railway running near power lines is subjected to electromagnetic interference from power lines, which can adversely affect normal operation of the railway’s communication and signaling system, resulting in safety hazard on the railway equipment and personnel. Therefore, it is very important to assess such electromagnetic interference issues to ensure the railway system’s reliability and the public safety. In this paper, a literature review is conducted on electromagnetic interference between railways and nearby power lines, which has not been widely researched in the past. Although IEEE Standard 2746-2020 has raised the importance of such electromagnetic interference, but it only offers very basic information without an in-depth coverage. This paper provides a review, where critical aspects of electromagnetic interference between railways and power lines are summarized and the future research direction in this area is recommended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.052
GPT teacher head0.370
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2021
Admission routes2
Has abstractyes

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