[article]
| Titre : |
Calculation of coating consumption quota for ship painting : a CS-GBRT approach |
| Type de document : |
texte imprimé |
| Auteurs : |
Henan Bu, Auteur ; Xingyu Ji, Auteur ; Xin Yuan, Auteur ; Ziyan Han, Auteur ; Lei Li, Auteur ; Zhuwen Yan, Auteur |
| Année de publication : |
2020 |
| Article en page(s) : |
p. 1597-1607 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Bateaux -- Revêtements:Bateaux -- Peinture
Calcul
Peinture -- Consommation
Prévision, Théorie de la
|
| Index. décimale : |
667.9 Revêtements et enduits |
| Résumé : |
This paper focuses on the prediction of the coating amount before the construction of ship painting, i.e., the calculation of coating consumption quota. At present, each shipyard uses a larger coating loss coefficient to calculate the coating consumption quota ; after the construction, there is often a lot of inventory left, which is not conducive to the scientific management of the ship coating process and the cost control of shipbuilding. Therefore, this paper proposes a coating consumption prediction method based on ensemble learning, using cosine similarity and gradient boosting regression tree hybrid algorithm (CS-GBRT) to calculate the coating loss coefficient under different working conditions. Cosine similarity is used to select similar data with less difference from the target to be predicted as the training set, and the loss function in GBRT is improved based on similarity weight to improve the prediction performance and calculation accuracy of GBRT. The coating data recorded by a shipyard from 2014 to 2019 are randomly selected to evaluate the prediction ability of the model established in the paper. The results show that when the proposed CS-GBRT algorithm is used to calculate the coating loss coefficient, the mean absolute error of training set and test set are both < 1.4, and the mean absolute error percentages are both < 4%. Compared with other research methods, the prediction accuracy is obviously improved, and the output feature importance is also consistent with the trend calculated by Spearman method, which proves the validity of the model again. |
| Note de contenu : |
- GBRT IMPROVED BY SIMILARITY : Ensemble learning method - Gradient boosting regression tree (GBRT) - Similarity calculation - Algorithm flow
- CONSTRUCTION OF COATING CONSUMPTION PREDICTION MODEL : Analysis of factors affecting coating consumption - Feature selection and data preprocessing - Feature coding and normalization - Feature importance analysis
- PREDICTION RESULTS ANALYSIS AND MODELS COMPARISON |
| DOI : |
https://doi.org/10.1007/s11998-020-00376-7 |
| En ligne : |
https://link.springer.com/content/pdf/10.1007/s11998-020-00376-7.pdf |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=34963 |
in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH > Vol. 17, N° 6 (11/2020) . - p. 1597-1607
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