纳米纤维
静电纺丝
学习迁移
人工智能
计算机科学
生成对抗网络
生成语法
卷积神经网络
过程(计算)
模式识别(心理学)
深度学习
机器学习
材料科学
纳米技术
复合材料
操作系统
聚合物
作者
Cosimo Ieracitano,Nadia Mammone,Annunziata Paviglianiti,F. Morabito
标识
DOI:10.1142/s012906572250054x
摘要
This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming.
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