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Record W2794184436 · doi:10.1109/tits.2017.2777982

Physics-Based Optimization of Access Point Placement for Train Communication Systems

2018· article· en· W2794184436 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Intelligent Transportation Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware deploymentPoint (geometry)WirelessComputer scienceCommunications systemOptimization problemPoint-to-pointEngineeringSimulationMathematical optimizationComputer networkTelecommunicationsAlgorithmMathematics

Abstract

fetched live from OpenAlex

Communication-based train-control (CBTC) systems are aimed at replacing conventional rail signaling with train control enabled by wireless communication between the train and a network of access points. The position of the access points has a significant impact on the performance of such a system. This paper presents an efficient optimization framework of access point placement through combining a site-specific electromagnetic simulator and an optimization algorithm. The aim of this approach is to select an optimal distribution of access points in order to maximize the coverage of the system. To that end, radio-wave propagation is modeled with the vector parabolic equation method, while the optimization of the access point location is pursued via the robust and well-convergent Hooke and Jeeves algorithm. Practical constraints and engineering considerations associated with the deployment of CBTC systems are taken into account. Numerical results are compared with experimental measurements in an actual CBTC deployment site, demonstrating the validity and usefulness of the proposed methodology.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.270
Teacher spread0.231 · 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