We propose a deep-learning architecture combined residual network (ResNet), graph convolutional network (GCN) and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of ResNet, GCN, an...
The authors first used a graph convolutional network (GCN) to learn the nodes’ representations in the graph for NER. The strategy used the syntactic relationships between words in a sentence to enhance NER performance. The CDR Dataset was used to evaluate the model's performance, and the ...
[16] adds classification based on inter-joint connection relationships and joint trajectory information to the ST-GCN algorithm for enhanced recognition. In the domain of non-image recognition, Ref. [17] employs accelerated data and compares the performance of three deep learning model architectures ...
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轴承故障全家桶更新 | CNN、LSTM、Transformer、TCN、串行、并行模型、时频图像、EMD分解等集合都在这里 05:31 Python轴承故障诊断 (19)基于Transformer-BiLSTM的创新诊断模型 05:04 注意力魔改 | 超强轴承故障诊断模型! 06:24 轴承故障全家桶更新 | 基于VGG16的时频图像分类算法 04:09 轴承故障—交叉...
Tian et al. [32] developed a graph-based approach for NER, where the input sentence is represented as a graph, and syntactic dependencies are used to construct the graph. The authors first used a graph convolutional network (GCN) to learn the nodes’ representations in the graph for NER. ...
轴承故障全家桶更新 | CNN、LSTM、Transformer、TCN、串行、并行模型、时频图像、EMD分解等集合都在这里 05:31 Python轴承故障诊断 (19)基于Transformer-BiLSTM的创新诊断模型 05:04 注意力魔改 | 超强轴承故障诊断模型! 06:24 轴承故障全家桶更新 | 基于VGG16的时频图像分类算法 04:09 轴承故障—交叉...