In the present study, the DEFORM computer code was used to develop two-dimensional and three-dimensional ®nite element models for simulating external thread rolling. To simulate rolling in two dimensions, a plane strain model was used where the thread is assumed to form through progressive penetration of the blank surface using a parallel set of wedge-shaped indenters. To develop the three-dimensional model, a¯at-die rolling process was simulated which incorporated blank rotation, die movement and pitch angle on the die faces. Based on a comparison of thread form and microhardness with as-rolled threads, the plane strain model was found to provide a reasonable approximation of thread-rolling behaviour. Results obtained from the initial pass of the three-dimensional model are promising although progress is currently limited by the excessive computational time needed, frequency of remeshing and sliding at the die±blank interface.
Every pixel in a hyperspectral image contains detailed spectral information in hundreds of narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral image is the cornerstone of various hyperspectral applications. Nowadays, deep learning models represented by the convolutional neural network (CNN) provides an ideal solution for feature extraction, and has made remarkable achievements in supervised hyperspectral classification. However, hyperspectral image annotation is time-consuming and laborious, and available training data is usually limited. Due to the “small-sample problem”, CNN-based hyperspectral classification is still challenging. Focused on the limited sample-based hyperspectral classification, we designed an 11-layer CNN model called R-HybridSN (Residual-HybridSN) from the perspective of network optimization. With an organic combination of 3D-2D-CNN, residual learning, and depth-separable convolutions, R-HybridSN can better learn deep hierarchical spatial–spectral features with very few training data. The performance of R-HybridSN is evaluated over three public available hyperspectral datasets on different amounts of training samples. Using only 5%, 1%, and 1% labeled data for training in Indian Pines, Salinas, and University of Pavia, respectively, the classification accuracy of R-HybridSN is 96.46%, 98.25%, 96.59%, respectively, which is far better than the contrast models.
Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on “small sample” hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial–spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial–spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial–spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.
Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained classification results. Although three-dimensional convolutional neural networks (3D-CNN) have been extensively investigated in hyperspectral image classification tasks and have made significant breakthroughs, hyperspectral classification under small sample conditions is still challenging. In order to facilitate small sample hyperspectral classification, a novel mixed spatial-spectral features cascade fusion network (MSSFN) is proposed. First, the covariance structure of hyperspectral data is modeled and dimensionality reduction is conducted using factor analysis. Then, two 3D spatial-spectral residual modules and one 2D separable spatial residual module are used to extract mixed spatial-spectral features. A cascade fusion pattern consisting of intra-block feature fusion and inter-block feature fusion is constructed to enhance the feature extraction capability. Finally, the second-order statistical information of the fused features is mined using second-order pooling and the classification is achieved by the fully connected layer after L2 normalization. On the three public available hyperspectral datasets, Indian Pines, Huston, and University of Pavia, only 5%, 3%, and 1% of the labeled samples were used for training, the accuracy of MSSFN in this paper is 98.52%, 96.30% and 98.83%, respectively, which is far better than the contrast models and verifies the effectiveness of MSSFN in small sample hyperspectral classification tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.