Near Real-Time Hydraulic Fracturing Event Recognition Using Deep Learning Methods

2020 ◽  
Vol 35 (03) ◽  
pp. 478-489 ◽  
Author(s):  
Yuchang Shen ◽  
Dingzhou Cao ◽  
Kate Ruddy ◽  
Luis Felipe Teixeira de Moraes
Keyword(s):  
Deep Learning ◽  
Hydraulic Fracturing ◽  
Real Time ◽  
Event Recognition ◽  
Learning Methods

Real-time multiple object tracking using deep learning methods

2021 ◽  
Author(s):  
Dimitrios Meimetis ◽  
Ioannis Daramouskas ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Learning Methods ◽  
Multiple Object

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

Ransomware Traffic Classification Using Deep Learning Models

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Arivudainambi D. ◽  
Varun Kumar K.A. ◽  
Vinoth Kumar R. ◽  
Visu P.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Classification Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Novel Method

Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In this research, the authors show how ransomware propagates and infects devices. Live traffic classifications of ransomware have been meticulously analyzed. Further, a novel method for the classification of ransomware traffic by using deep learning methods is presented. Based on classification, the detection of ransomware is approached with the characteristics of the network traffic and its communications. In more detail, the behavior of popular ransomware, Crypto Wall, is analyzed and based on this knowledge, a real-time ransomware live traffic classification model is proposed.

scholarly journals Application of Deep-Learning Methods to Real Time Face Mask Detection

2021 ◽  
Vol 19 (6) ◽  
pp. 994-1001
Author(s):  
Diego Gonzalez Dondo ◽  
Javier Andres Redolfi ◽  
R. Gaston Araguas ◽  
Daiana Garcia
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Methods

scholarly journals Building a Real-Time 2D Lidar Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Nadim Arubai ◽  
Omar Hamdoun ◽  
Assef Jafar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Tilt Angle ◽  
State Of The Art ◽  
The State ◽  
Prediction Problem ◽  
Learning Methods ◽  
Depth Prediction ◽  
Avoidance Problem

Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.

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