Injun Lee
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Injun Lee has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Jaesool Shim
School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
1 shared publication - Kyung-Chul Cho
School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
1 shared publication - Si-Woo Park
Energy System Research Center, Korea Textile Machinery Convergence Research Institute, Gyeongsan 38542, Republic of Korea
1 shared publication
Latest Publications
- Open Access
Process Prediction and Feature Visualization of Meltblown Nonwoven Fabrics Using Scanning Electron Microscopic (SEM) Image-Based Deep Neural Network Algorithms
Meltblown nonwoven fabrics are used in various products, such as masks, protective clothing, industrial filters, and sanitary products. As the range of products incorporating meltblown nonwoven fabrics has recently expanded, numerous studies have been conducted to explore the correlation between production process conditions and the performance of meltblown nonwoven fabrics. Deep neural network algorithms, including convolutional neural networks (CNNs), have been widely applied in numerous industries for tasks such as object detection, recognition, classification, and fault detection. In this study, the correlation between the meltblown nonwoven fabric production process and performance was analyzed using deep neural network algorithms for classifying SEM images. The SEM images of meltblown nonwovens produced under various process conditions were trained using well-known convolutional neural network models (VGG16, VGG19, ResNet50, and DenseNet121), and each model showed high accuracy ranging from 95% to 99%. In addition, LRP (Layer-wise Relevance Propagation) and Gradient-weighted Class Activation Mapping (Grad-CAM) models were applied to visualize and analyze the characteristics and correlation of the SEM images to predict the meltblown nonwoven fabric production process.
Open AccessProcess Prediction and Feature Visualization of Meltblown Nonwoven Fabrics Using Scanning Electron Microscopic (SEM) Image-Based Deep Neural Network Algorithms
Meltblown nonwoven fabrics are used in various products, such as masks, protective clothing, industrial filters, and sanitary products. As the range of products incorporating meltblown nonwoven fabrics has recently expanded, numerous studies have been conducted to explore the correlation between production process conditions and the performance of meltblown nonwoven fabrics. Deep neural network algorithms, including convolutional neural networks (CNNs), have been widely applied in numerous industries for tasks such as object detection, recognition, classification, and fault detection. In this study, the correlation between the meltblown nonwoven fabric production process and performance was analyzed using deep neural network algorithms for classifying SEM images. The SEM images of meltblown nonwovens produced under various process conditions were trained using well-known convolutional neural network models (VGG16, VGG19, ResNet50, and DenseNet121), and each model showed high accuracy ranging from 95% to 99%. In addition, LRP (Layer-wise Relevance Propagation) and Gradient-weighted Class Activation Mapping (Grad-CAM) models were applied to visualize and analyze the characteristics and correlation of the SEM images to predict the meltblown nonwoven fabric production process.