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Record W4385431819 · doi:10.18280/jesa.560302

Influence of Time Delays on Network-Controlled Diesel Generator Performance

2023· article· fr· W4385431819 on OpenAlexvenueno aff
Mahmoud M. S. Al-Suod

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2023
Typearticle
Languagefr
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsGenerator (circuit theory)Diesel generatorDiesel fuelAutomotive engineeringEnvironmental scienceComputer scienceEngineeringPhysicsPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

In the paper the influence of Ethernet network dynamics on the quality of diesel-generator control is considered.The control quality indicators depend on time delays in the transmission of data packets over the Ethernet network.The optimization task of minimizing such time delays to improve the control quality was resolved.The Lagrange's method of undetermined multipliers and Bellman optimality rule were used for the analytical solution of the problem of minimizing time delays.A Matlab-model was developed for the research of the impact of time delays on the diesel-generator control quality, in which the Ethernet network is used as a data transmission channel between control objects and regulators.The scientific novelty of the results is in the improvement of the analytical method for analysing the characteristics of the automated control systems information processing network to study the influence of network dynamics on the quality of control of diesel-generators and determining the intensity of transmission of information and control packets, as well as using the proposed optimal conflict resolution rule, using which the transmission time delays data are minimal.This reduces the number of conflicts between the processes claiming the resources by almost 2 times and increases the quality of control.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.229
Teacher spread0.217 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicPower Systems and Renewable EnergyFrench-language works237,207