针对注塑机结构设计工作中对数据管理系统的需求,从优化数据管理,实现协同设计和提高设计效率的目的出发,研究了注塑机结构设计部门级产品数据库管理(Product Data Management,PDM)系统实施和综合应用。从整体上缩短了注塑机产品的开发...针对注塑机结构设计工作中对数据管理系统的需求,从优化数据管理,实现协同设计和提高设计效率的目的出发,研究了注塑机结构设计部门级产品数据库管理(Product Data Management,PDM)系统实施和综合应用。从整体上缩短了注塑机产品的开发周期。展开更多
Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different...Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.展开更多
基金supported by the project“ZeDaBase-Batteriezelldatenbank”of the Initiative and Networking Fund of the Helmholtz Association(KW-BASF-6).
文摘Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.
