.png){kind=icon}
返回
Sampling via the aggregation value for data-driven manufacturing
.png){kind=icon}
OA
AI
DOI：10.1093/nsr/nwac201.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven modelling has shown promising potential in many industrial applications, while the expensive and time-consuming labelling of experimental and simulation data restricts its further development. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. In this research, the aggregation value is defined using a novel representation for the value of a sample set by modelling the invisible redundant information as the overlaps of neighbouring values. The sampling problem is hence converted to the maximisation of the submodular function over the aggregation value. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods. The research outcome also indicates its appealing potential to reduce labelling efforts for more data-scarcity scenarios. A new concept, aggregation value, is proposed to evaluate the real value of sample set, thus high-value sampling can be ensured which will greatly reduce the labelling effort for machine learning tasks.
Keyword:
data-driven modelling
intelligent manufacturing
data sampling
data value
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
17.1
论文数:
3.7K
被引数:
2.0W
机构
引用论文
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture用于在制造过程中对复合工具系统的热化学固化过程进行建模的物理通知神经网络OAAI
OAAI分享收藏Predicting future dynamics from short-term time series using an Anticipated Learning Machine使用预期学习机从短期时间序列预测未来动态.png)
NATIONAL SCIENCE REVIEWOAAI
NATIONAL SCIENCE REVIEWIF17.1
OAAI分享收藏A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design支持基于仿真的复杂工程设计的全局元建模自适应采样研究
PREAI
分享收藏
查看更多内容