scholarly journals Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

2020 ◽  
Vol 85 (4) ◽  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Supervised Training ◽  
Unsupervised Training

scholarly journals Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

2020 ◽  
Vol 85 (4) ◽  
pp. 2263-2277
Author(s):  
Ramin Jafari ◽  
Pascal Spincemaille ◽  
Jinwei Zhang ◽  
Thanh D. Nguyen ◽  
Xianfu Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Supervised Training ◽  
Unsupervised Training

scholarly journals A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1619
Author(s):  
Kim ◽  
Sok ◽  
Kang ◽  
Lee ◽  
Nam
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Deep Neural Network ◽  
Phase Estimation ◽  
Fault Current ◽  
Dc Offset ◽  
Intensive Training ◽  
Unsupervised Training ◽  
Training Methodologies ◽  
Hidden Layer

The purpose of this paper is to remove the exponentially decaying DC offset in fault current waveforms using a deep neural network (DNN), even under harmonics and noise distortion. The DNN is implemented using the TensorFlow library based on Python. Autoencoders are utilized to determine the number of neurons in each hidden layer. Then, the number of hidden layers is experimentally decided by comparing the performance of DNNs with different numbers of hidden layers. Once the optimal DNN size has been determined, intensive training is performed using both the supervised and unsupervised training methodologies. Through various case studies, it was verified that the DNN is immune to harmonics, noise distortion, and variation of the time constant of the DC offset. In addition, it was found that the DNN can be applied to power systems with different voltage levels.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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