ESTIMASI BIAYA DAUR HIDUP DAN OPTIMISASI HARGA PESAWAT TERBANG
Bibliographic record
Abstract
Since the conceptual design phase, the estimate of airplane life cycle cost (LCC) is carried out to support a decision making process. The LCC consist of research, development, testing, and evaluation cost, where an airplane estimated price (AEP) is a part of this calculation. Furthermore, AEP is employed as a non linear objective funtion for developing a constrained optimization algorthm. Rosen’s gradient projection is applyed in the development of computer program named Cost Analysis implementing object oriented approach on Microsoft Visual C++ 6.0. In order to verify the application, some data of Ourania jet airplane were utilized for carrying out a case study. The result of Cost Analysis shows that the estimated LCC are similar to the reference. Also, the optimization problems can be solved by Rosen’s algorithm less than ten iterations. Keywords:Conceptual aircraft design, Life cycle cost, Object oriented approach, Visual C++
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".