Statistical Machine Learning with Applications in Industrial Processes (in cooperation with Tata Steel)

统计机器学习及其在工业过程中的应用（与塔塔钢铁公司合作）

• 批准号: 1935144
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1935144
• 关键词: Statistical; Machine; Learning; Applications; Industrial

Recent innovations in machine learning has been largely driven by the emergence of Big Data that are correlated, high-dimensional, dynamic and often not in the Euclidean space. The PhD project is aimed at developing novel statistical machine learning methodology andalgorithms to address the modelling challenges that arise. The project is motivated by the need for building predictive models to understand steel manufacturing processes. Currently we are exploring possible applications of network science, looking for a new generative network model that incorporates covariate information.

机器学习领域最近的创新主要是由大数据的出现所推动的，这些大数据是相关的、高维的、动态的，而且往往不在欧几里得空间中。该博士项目旨在开发新的统计机器学习方法和算法，以解决出现的建模挑战。该项目的动机是需要建立预测模型，以了解钢铁制造过程。目前，我们正在探索网络科学的可能应用，寻找一个新的生成网络模型，结合协变量信息。

项目成果

A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency

• DOI: 10.1016/j.commatsci.2020.110053
• 发表时间: 2021-01
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Stefan Stein;Chenlei Leng;S. Thornton;Michel F. Randrianandrasana