Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework.

ABSTRACT: Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performan

SUBMITTER: Ke J 

PROVIDER: S-EPMC11398124 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature