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

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.

Download Full-text

High-Precision Power Load Forecasting Using Real-time Temperature Information and Deep Learning Method

Electric Power Components and Systems ◽

10.1080/15325008.2019.1689451 ◽

2019 ◽

Vol 47 (16-17) ◽

pp. 1574-1583 ◽

Cited By ~ 1

Author(s):

Xianfeng Xu ◽

Mei Gong ◽

Liuyang Huang ◽

Ahui Liu

Keyword(s):

Deep Learning ◽

Real Time ◽

High Precision ◽

Load Forecasting ◽

Learning Method ◽

Power Load ◽

Power Load Forecasting

Download Full-text

Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP): A Novel Deep-Learning Method for Real-Time Prostate Treatment Planning

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2020.07.2261 ◽

2020 ◽

Vol 108 (3) ◽

pp. S93-S94

Author(s):

Y. Ni ◽

Y. Sheng ◽

J. Zhang ◽

X. Li ◽

Q.J.J. Wu ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Treatment Planning ◽

Learning Method ◽

Sequence Prediction

Download Full-text

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

Advanced Materials Technologies ◽

10.1002/admt.201800167 ◽

2018 ◽

Vol 4 (1) ◽

pp. 1800167 ◽

Cited By ~ 16

Author(s):

Guangquan Zhao ◽

Jin Yang ◽

Jun Chen ◽

Guang Zhu ◽

Zedong Jiang ◽

...

Keyword(s):

Deep Learning ◽

Triboelectric Nanogenerator ◽

Learning Method ◽

Keystroke Dynamics ◽

Dynamics Identification

Download Full-text

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

Applied Sciences ◽

10.3390/app9091823 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1823 ◽

Cited By ~ 16

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.

Download Full-text