Particle-Laden Droplet-Driven Triboelectric Nanogenerator for Real-Time Sediment Monitoring Using a Deep Learning Method

2020 ◽  
Vol 12 (34) ◽  
pp. 38192-38201 ◽  
Author(s):  
Lei Yang ◽  
Yunfei Wang ◽  
Zhibin Zhao ◽  
Yanjie Guo ◽  
Sicheng Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Triboelectric Nanogenerator ◽  
Learning Method ◽  
Sediment Monitoring

SiamFPN: A Deep Learning Method for Accurate and Real-time Maritime Ship Tracking

2020 ◽  
pp. 1-1
Author(s):  
Yunxiao Shan ◽  
Xiaomei Zhou ◽  
Shanghua Liu ◽  
Yunfei Zhang ◽  
Kai Huang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Method

scholarly journals Real-Time Data Filling and Automatic Retrieval Algorithm of Road Traffic Based on Deep-Learning Method

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Jie Zhu ◽  
Weixiang Xu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Road Traffic ◽  
Retrieval Algorithm ◽  
Target Area ◽  
Learning Method ◽  
Time Data ◽  
The Real ◽  
Automatic Retrieval ◽  
Real Time Data

In order to enhance the real-time and retrieval performance of road traffic data filling, a real-time data filling and automatic retrieval algorithm based on the deep-learning method is proposed. In image detection, the depth representation is extracted according to the detection target area of a general object. The local invariant feature is extracted to describe local attributes in the region, and it is fused with depth representation to complete the real-time data filling of road traffic. According to the results of the database enhancement, the retrieval results of the deep representation level are reordered. In the index stage, unsupervised feature updating is realized by neighborhood information to improve the performance of a feature retrieval. The experimental results show that the proposed method has high recall and precision, a short retrieval time and a low running cost.

Keystroke Dynamics Identification Based on Triboelectric Nanogenerator for Intelligent Keyboard Using Deep Learning Method

2018 ◽  
Vol 4 (1) ◽  
pp. 1800167 ◽  
Author(s):  
Guangquan Zhao ◽  
Jin Yang ◽  
Jun Chen ◽  
Guang Zhu ◽  
Zedong Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Triboelectric Nanogenerator ◽  
Learning Method ◽  
Keystroke Dynamics ◽  
Dynamics Identification

scholarly journals A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

2019 ◽  
Vol 9 (9) ◽  
pp. 1823 ◽  
Author(s):  
Zilong Zhuang ◽  
Huichun Lv ◽  
Jie Xu ◽  
Zizhao Huang ◽  
Wei Qin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Learning Method ◽  
Residual Network ◽  
Bearing Fault Diagnosis ◽  
Dilated Convolution ◽  
Gate Structure ◽  
Input Gate

Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.

Monitoring Suspended Sediment Particles Based on Triboelectric Nanogenerator and Deep Learning Method

Nano Energy ◽  
2021 ◽  
pp. 106698
Author(s):  
Jian Yu ◽  
Yu Wen ◽  
Lei Yang ◽  
Zhibin Zhao ◽  
Yanjie Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Suspended Sediment ◽  
Triboelectric Nanogenerator ◽  
Learning Method ◽  
Sediment Particles
Sign in / Sign up

Export Citation Format

Share Document