Development of Costing and Budget Control Strategy for Shipbuilding Based on Machine Learning
Bibliographic record
Abstract
Due to the complexity of the ship product structure and process, long production cycle and other factors, ship enterprises are plagued by the problem of profitability.Strengthening cost prediction and budget control is a very important means for ship enterprises to improve their profit margins.By analyzing the cost structure of shipbuilding, this paper proposes a rolling forecast model of shipbuilding cost based on long and short-term memory neural network (LSTM) as the estimation method of shipbuilding cost.Meanwhile, the traditional earned value method and target cost method are combined to sort out the shipbuilding cost control process and prepare the cost control plan as the control strategy of shipbuilding cost.Then we take the manufacturing data of a shipyard as the experimental object, use this paper's model for data mining, compare the data performance of this paper's model with similar algorithms, and verify the feasibility of this paper's model.Finally, the model of this paper is applied to real cases.In the comparison of the estimation results between this paper's model and the commonly used algorithms, the average error of cost estimation of this paper's model is ±4.95%, which is better than the average error of the commonly used algorithms.The superior accuracy of this paper's model in shipbuilding cost estimation is verified.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".