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Record W2434702020 · doi:10.1109/mownet.2016.7496613

Performance of WiMAX for smart grid applications

2016· article· en· W2434702020 on OpenAlexaff
Oana Neagu, Walaa Hamouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsConcordia University
Fundersnot available
KeywordsSmart gridWiMAXComputer scienceComputer networkNetwork packetInteroperabilityScalabilityBase stationTelecommunicationsEngineeringElectrical engineeringWirelessDatabase

Abstract

fetched live from OpenAlex

The traditional power grid system is a centralized and unidirectional system where the power flows from the power generation and distribution station and to the consumer. The smart grid aims to improve certain parameters of the classical power grid by providing more reliable and efficient use of electricity, a two-way flow of information between users and the utility station, improved energy storage and to incorporate renewable sources of energy, while being scalable and self-healable. In this paper, we analyze the performance of a smart grid in which the communication layer is implemented using Worldwide Interoperability for Microwave Access (WiMAX) communication technology. Parameters such as throughput, network capacity, packet loss, latency are studied by analyzing the traffic model generated by using several applications in the Distribution Area Network (DAN) of the smart grid. The applications whose traffic was simulated using OPNET are the following four: metering and pricing, electrical car, video surveillance and voice support for workforce. The capacity of a base station in the distribution area network is obtained for each smart grid application individually as well as for the combined traffic of all applications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.086

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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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 designNot applicable
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

Citations12
Published2016
Admission routes1
Has abstractyes

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