A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties

Xue, Zheng; Li, Tao; Peng, Shitong; Zhang, Chao-yong; Zhang, Hongchao

doi:10.1007/s11771-022-4906-z

Cited by 4 publications

(1 citation statement)

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“…One of the pooling operations is performed along the channel axis, i.e., each pooling compares values between different channels rather than values in different regions of the same channel. [21] is the core structure of the residual network [22], which mainly contains three convolutional layers, as shown in Figure 1. The size of the convolution kernel in the first layer is 1 × 1, which is mainly aimed at reducing the dimensionality of the feature map and thus the number of network parameters.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Feature Fusion Attention Lightweight Facial Expression Recognition

Zhang

2022

International Journal of Aerospace Engineering

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Facial expression recognition based on residual networks is important for technologies related to space human-robot interaction and collaboration but suffers from low accuracy and slow computation in complex network structures. To solve these problems, this paper proposes a multiscale feature fusion attention lightweight wide residual network. The network first uses an improved random erasing method to preprocess facial expression images, which improves the generalizability of the model. The use of a modified depthwise separable convolution in the feature extraction network reduces the computational effort associated with the network parameters and enhances the characterization of the extracted features through a channel shuffle operation. Then, an improved bottleneck block is used to reduce the dimensionality of the upper layer network feature map to further reduce the number of network parameters while enhancing the network feature extraction capability. Finally, an optimized multiscale feature lightweight attention mechanism module is embedded to further improve the feature extractability of the network for human facial expressions. The experimental results show that the accuracy of the model is 73.21%, 98.72%, and 95.21% on FER2013, CK+ and JAFFE, respectively, with a covariance of 10.14 M. Compared with other networks, the model proposed in this paper has faster computing speed and better accuracy at the same time.

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Section: Related Workmentioning

confidence: 99%