scholarly journals A Vehicle Recognition Algorithm Based on Deep Convolution Neural Network

2020 ◽  
Vol 37 (4) ◽  
pp. 647-653
Author(s):  
Yang Yang
Keyword(s):  
Neural Network ◽  
Recognition Algorithm ◽  
Convolution Neural Network ◽  
Vehicle Recognition ◽  
Deep Convolution Neural Network

scholarly journals Graphite Classification Based on Improved Convolution Neural Network

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1995
Author(s):  
Guangjun Liu ◽  
Xiaoping Xu ◽  
Xiangjia Yu ◽  
Feng Wang
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Convolution Neural Network ◽  
High Tech ◽  
Data Set ◽  
Output Module ◽  
Deep Convolution Neural Network ◽  
Migration Network ◽  
Visual Geometry

In the development of high-tech industries, graphite has become increasingly more important. The world has gradually entered the graphite era from the silicon era. In order to make good use of high-quality graphite resources, a graphite classification and recognition algorithm based on an improved convolution neural network is proposed in this paper. Based on the self-built initial data set, the offline expansion and online enhancement of the data set can effectively expand the data set and reduce the risk of deep convolution neural network overfitting. Based on the visual geometry group 16 (VGG16), residual net 34 (ResNet34), and mobile net Vision 2 (MobileNet V2), a new output module is redesigned and loaded into the full connection layer. The improved migration network enhances the generalization ability and robustness of the model; moreover, combined with the focal loss function, the superparameters of the model are modified and trained on the basis of the graphite data set. The simulation results illustrate that the recognition accuracy of the proposed method is significantly improved, the convergence speed is accelerated, and the model is more stable, which proves the feasibility and effectiveness of the proposed method.

scholarly journals A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition

2017 ◽  
Vol 107 ◽  
pp. 715-720 ◽  
Author(s):  
Xingcheng Luo ◽  
Ruihan Shen ◽  
Jian Hu ◽  
Jianhua Deng ◽  
Linji Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Vehicle Recognition ◽  
Deep Convolution Neural Network

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

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