Large Scale Indexing and Searching Deep Convolutional Neural Network Features

2016 ◽  
pp. 213-224 ◽  
Author(s):  
Giuseppe Amato ◽  
Franca Debole ◽  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
Fausto Rabitti
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Deep Convolutional Neural Network

Classification of large-scale stellar spectra based on deep convolutional neural network

2018 ◽  
Vol 483 (4) ◽  
pp. 4774-4783 ◽  
Author(s):  
W Liu ◽  
M Zhu ◽  
C Dai ◽  
D Y He ◽  
Jiawen Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Deep Convolutional Neural Network ◽  
Stellar Spectra

Optimization of deep convolutional neural network for large scale image retrieval

2018 ◽  
Vol 303 ◽  
pp. 60-67 ◽  
Author(s):  
Cong Bai ◽  
Ling Huang ◽  
Xiang Pan ◽  
Jianwei Zheng ◽  
Shengyong Chen
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Deep Convolutional Neural Network ◽  
Large Scale Image Retrieval

scholarly journals Anomaly Detection of Electro-data Based on Deep Convolutional Neural Network

2018 ◽  
Vol 173 ◽  
pp. 03080
Author(s):  
Zhi Zhang ◽  
Liang Guo ◽  
Xianguang Dong ◽  
Yanjie Dai ◽  
Yan Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Recall Rate ◽  
Deep Convolutional Neural Network ◽  
Electrical Parameters ◽  
Sample Data ◽  
Novel Method ◽  
Time Information ◽  
Precision Rate

As diversity of electro-data anomaly, the methods based on artificial feature are becoming more difficult to detect anomalies among a great deal of electro-data. Hence, this paper proposes a novel method which is based on deep convolutional neural network (DCNN) to detect anomaly electro-data. This method models the sample data with time information and electrical parameters, and labels them as normal or abnormal automatically. Further, the paper improves the designing DCNN to extract precise features from large scale of electro-data to get high accuracy. The results of the case analysis show that our method can detect anomaly electro-data more exact and stable than the traditional methods. The abnormal precision rate and abnormal recall rate of our approach reach 92.7% and 91.3% respectively.

scholarly journals Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 818 ◽  
Author(s):  
Xiaolei Ma ◽  
Zhuang Dai ◽  
Zhengbing He ◽  
Jihui Ma ◽  
Yong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Transportation Network ◽  
Deep Convolutional Neural Network ◽  
Speed Prediction ◽  
Network Speed

Radar Super Resolution Using a Deep Convolutional Neural Network

2020 ◽  
Vol 37 (12) ◽  
pp. 2197-2207
Author(s):  
Andrew Geiss ◽  
Joseph C. Hardin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Super Resolution ◽  
Deep Convolutional Neural Network ◽  
Perceptual Quality ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Pixel Scale ◽  
Made In

AbstractSuper resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.

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