Name
Abstract | ||
|---|---|---|
This paper aims to predict fabric colours by analyzing the relationship between multiple process parameters and colours of dyed fabrics in pad dyeing. The task is approached as a multi-dimensional regression problem. Within the framework of machine learning designed for colour prediction, two models, back-propagation neural network (BPNN) and multi-dimensional support vector regressor (M-SVR) are implemented. The process parameters are fed to these multi-dimensional regression models to predict the fabric colours measured in CIELAB values. The raw data used in our study are directly provided by a dyeing and printing manufacturer. As our experiments show, BPNN outperforms M-SVR with a relatively large data set while M-SVR is more accurate than BPNN is with a relatively small data set. |
Year | Venue | Field |
|---|---|---|
2018 | ICCCS | Multi dimensional,Small data,Regression,Pattern recognition,Regression analysis,Computer science,Support vector machine,Raw data,Real-time computing,Artificial intelligence,Artificial neural network,Dyeing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Chen | 1 | 76 | 25.75 |
| Chengzhi Zhou | 2 | 0 | 0.34 |
| Yijun Zhou | 3 | 0 | 0.68 |
| Lingyun Zhu | 4 | 39 | 5.48 |
| Ting Lu | 5 | 87 | 10.95 |
| Guo-hua Liu | 6 | 102 | 34.53 |