A5031319754- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Face and Expression Recognition
- Remote Sensing and Land Use
- Image Retrieval and Classification Techniques
- Fault Detection and Control Systems
- Optimization and Packing Problems
- Geochemistry and Geologic Mapping
- Text and Document Classification Technologies
- Plant nutrient uptake and metabolism
- Scheduling and Optimization Algorithms
- Robotic Path Planning Algorithms
- Advanced Chemical Sensor Technologies
- Advanced Graph Neural Networks
- Advanced Manufacturing and Logistics Optimization
- Advanced Control Systems Optimization
- Mineral Processing and Grinding
- Manufacturing Process and Optimization
- Advanced Image and Video Retrieval Techniques
- Remote Sensing in Agriculture
- Vehicle Routing Optimization Methods
- Legume Nitrogen Fixing Symbiosis
- Wastewater Treatment and Nitrogen Removal
- Visual Attention and Saliency Detection
Xi'an High Tech University
2021-2025
Shihezi University
2025
Shenyang University of Chemical Technology
2024
Jilin Agricultural University
2023
Shandong University
2017
The application of graph convolutional networks (GCNs) to hyperspectral image (HSI) classification is a heavily researched topic. However, GCNs are based on spectral filters, which computationally costly and fail suppress noise effectively. In addition, the current GCN-based methods prone oversmoothing (the representation each node tends be congruent) problems. To circumvent these problems, novel semi-supervised locality-preserving dense neural network (GNN) with autoregressive moving...
With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to semi-supervised classification, and the application GNN images attracted much attention. However, existing GNN-based methods single or filter mainly used extract HSI features, which does not take full advantage various networks (graph filters). Moreover, traditional GNNs have problem oversmoothing. To...
Recently, graph convolutional network (GCN) has achieved promising results in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which difficult to aggregate the new node. Besides, existing GCN-based methods divide construction and classification into two stages ignoring influence of constructed error on results. Moreover, available fail understand global contextual information graph. In this article, we propose novel multiscale sample with...
Due to prior knowledge deficiency, large spectral variability, and high dimension of hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task. Deep methods have achieved remarkable success attracted increasing attention in unsupervised classification (HSIC). However, the poor robustness, adaptability, feature presentation limit their practical applications complex large-scale datasets. Thus, this article introduces novel self-supervised locality preserving...
Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with no labeled samples. Deep methods have attracted increasing attention and achieved remarkable success in HSI classification. However, most existing are ineffective for large-scale HSI, due to their poor robustness, adaptability, feature presentation. In this paper, address these issues, we introduce unsupervised self-correlated learning smoothy enhanced locality preserving graph convolution embedding...
Graph convolutional network (GCN) has shown potential in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which difficult to aggregate the new node. The available GCN-based methods fail understand global and contextual information of graph. To address this deficiency, novel semisupervised based on graph sample aggregate-attention (SAGE-A) for HSIs' classification proposed. Different from SAGE-A adopts multilevel (graphSAGE) network, as it can flexibly...
Graph convolutional networks (GCN) have begun to show their potential in hyperspectral image classification recent years. However, most of the current GCN methods are designed learn node features on fixed and homogeneous graphs, it is difficult for them effective heterogeneous graphs. The limitation particularly evident because different types nodes edges. Transformer Network with graph attention mechanism (GTN-A) proposed address this shortcoming paper. It can generate a new structure,...
In recent years, empowered by artificial intelligence technologies, computer-assisted language learning systems have gradually become a hot topic of research. Currently, the mainstream pronunciation assessment models rely on advanced speech recognition technology, converting into phoneme sequences, and then determining mispronounced phonemes through sequence comparison. To optimize task in evaluation, this paper proposes Chinese model based improved Zipformer-RNN-T(Pruned) architecture,...
Abstract Stacked autoencoders (SAEs) have great potential in developing soft sensors due to their excellent feature extraction capabilities. However, the pre‐training stage of SAE is unsupervised and some important information related target variables may be discarded. Meanwhile, as depth network increases, reconstruction errors continue accumulate, resulting incomplete representations original input. In addition, dynamic nature data affects predictive results model. To address these issues,...
Nitrification inhibitors (NIs) have been widely applied to inhibit nitrification and reduce N2O emissions in agriculture. However, there are still some shortcomings, e.g. short effective periods, large applying amounts, low effectiveness, easy deactivation different effect. Thus, a nitrapyrin microcapsule suspension (CPCS) was used as new experimental material elaborate its effects on nitrogen transformation microbial response mechanisms black soil by cultivation experiments with six...