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Record W3045091765 · doi:10.1115/jrc2020-8003

Access Point Placement Optimization for a CBTC System Wireless Data Communication Network

2020· article· en· W3045091765 on OpenAlexaff
Arash Aziminejad, Yan He

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsComputer scienceWirelessHeuristicCommunications systemSoftware deploymentConvex optimizationComputer networkWireless networkTransceiverWireless site surveyReal-time computingWi-Fi arrayTelecommunicationsRegular polygon

Abstract

fetched live from OpenAlex

Abstract Radio-based Communication-Based Train Control (CBTC) systems are widely utilized in major urban centers around the world to improve capacity, performance, and safety of public rail transportation systems. The system primary functionalities are performed based on the wireless mobile communication media, through which wayside-onboard communication data links are established. The focus of the presented research is to improve the performance of the CBTC wireless network by providing an efficient framework for placement optimization of the wayside transceivers aiming to maximize the radio coverage. The QoS-oriented convex optimization cost function is based on a heuristic model of radio wave propagation in the tunnel environment. The optimization engine uses the robust, efficient, and well-behaved Nelder-Mead algorithm. Furthermore, to provide reliable means for verification, numerical results are compared with measured data produced through an empirical site survey process performed for an actual CBTC system deployment.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.390

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 science0.0010.000
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 head0.051
GPT teacher head0.254
Teacher spread0.203 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2020
Admission routes1
Has abstractyes

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