.png){kind=icon}
返回
Multi-Task Deep Learning Methods for Determining Railway Major Technical Standards
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2023.3338146.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Railway major technical standards (RMTSs) are prerequisites for subsequent railway design processes, which have fundamental influences on controlling railway transport capacity and investment. However, the traditional manual decision process is laborious and time-consuming. It also has difficulties in quantifying the interrelations among different standard parameters. In this paper, we propose a so-called voting election framework for efficient RMTS decision-making, in which three stages, namely constituency partition, constituency election and ballot summary, are developed. Then, for constituency election, four types of multi-task neural networks, namely, cascaded, overall parallel, grouped parallel and hybrid multi-task neural networks, are designed to identify the interrelations among six kinds of RMTSs. Besides, hyperparameter fine-tuning experiments are conducted to optimize the performance of the proposed networks. Finally, based on a dataset comprising realistic railway cases, it is found that the grouped parallel multi-task neural network performs the best by achieving an average decision accuracy of 86.9% compared to manual decision results and by outperforming the previous single-task neural network method by 10.7% in average decision accuracy.
Keyword:
Decision making
multi-task deep learning
neural network architecture
railway technical standards
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Electrostatic Fields near the Active Site of Human Aldose Reductase: 2. New Inhibitors and Complications Caused by Hydrogen Bonds.png)
Biochemistry
OAAI
BiochemistryIF0
OAAI分享收藏A novel and efficient xanthenic dye–organometallic ion‐pair complex for photoinitiating polymerization一种用于光引发聚合的新型高效的黄原胶染料-有机金属离子对配合物
PREAI
分享收藏
查看更多内容