Influence of Time Delays on Network-Controlled Diesel Generator Performance
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".