Application of Subtraction Average-Based Optimizer to Selected Electrical and Mechanical Engineering Problems
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Bibliographic record
Abstract
Summary Mechatronics engineering typically involves dealing with challenges related to designing and upkeeping mechanical and electrical systems. The subtractive averaging-based optimization algorithm is a commonly used method for minimizing system errors by iteratively adjusting parameters to enhance system performance and stability. In this thesis, the advantages and disadvantages of this optimization algorithm in solving the corresponding problems are investigated by applying the subtractive averaging optimizer to some electromechanical engineering problems. The final optimization results in solving Gas Transmission Compressor Design and Planetary Gear Train Design Optimization are very similar to the theoretical values, but for Optimal Setting of Droop Controller for Minimization of Reactive Power Loss in Islanded Microgrids problem the optimization values are more different from the theoretical values. It was found that the subtractive averaging optimizer is beneficial for solving electromechanical engineering problems in some specific problems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it