Bibliographic record
Abstract
In medium voltage electrical distribution networks, prevent the loss reduction is very important and certainly in line with this, system engineering issue and use of proper equipment, a good work has been done.Development of distribution systems result in higher system losses and poor voltage regulation.Consequently, an efficient and effective distribution system has become more urgent and important.Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line.Evaluation aging conductors for losses and costs imposed in addition to the careful planning of technical and economic networks can be identified in the network design.This paper examines the use of different evolutionary algorithms, imperialist competitive algorithm (ICA) to optimal branch conductor Selection and Reconstruction In view of the aging conductors in planning radial distribution systems with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors in order to improve productivity.Simulations are carried out on 69-bus radial distribution network using ICA approaches in order to show the accuracy as well as the efficiency of the proposed solution technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.935 | 0.951 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".