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Record W3091942015 · doi:10.1109/jsyst.2020.3025202

Mobile Communications-Enabled Smart Grid Cosimulator System Design

2020· article· en· W3091942015 on OpenAlexafffund
Kevin Shimotakahara, Medhat Elsayed, Melike Erol‐Kantarci, Karin Hinzer

Bibliographic record

VenueIEEE Systems Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Ottawa
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsComputer scienceCommunications protocolProtocol stackWirelessCommunications systemGridEmbedded systemReal-time computingDistributed computingSimulationComputer networkWireless sensor network

Abstract

fetched live from OpenAlex

In this article, we present a system design approach for a simulator constructed in MATLAB/Simulink used to simulate power systems and the underlying communication system, i.e., device-to-device (D2D) communication protocol stack of long-term evolution (LTE). Emphasis is placed on the characterization of the continuous time-discrete event (CT-DE) cosimulation interface used to develop this simulator. We model the mapping of communication links between D2D nodes as a bipartite digraph, and proceed to characterize the dynamics of information exchange between power system signals and LTE nodes using the discrete event specification formalism. The joint CT-DE dynamics were tested with a simple fault location, isolation, and restoration application. The results indicated that the control signal shifts in the power system simulator were synchronized with the appropriate LTE messaging dynamics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes2
Has abstractyes

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