ART-2 neural network usage to determine moment of slag discharge during steel teeming process

2017 ◽  
Author(s):  
Y. I. Eremenko ◽  
D. A. Poleshchenko ◽  
A. I. Glushchenko ◽  
Yu. A. Tsygankov ◽  
Yu. A. Kovriznich
Keyword(s):  
Neural Network ◽  
Network Usage

scholarly journals ATRIAL FIBRILLATION DETECTION ON ELECTROCARDIOGRAMS WITH CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol 9 (4) ◽  
pp. 69-73
Author(s):  
Viktor Kifer ◽  
Natalia Zagorodna ◽  
Olena Hevko
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Deep Neural Network ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Noisy Signals ◽  
Network Usage ◽  
Model Training

In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.

About Heating Plants Control System Developing on Basis of Neural Network Usage for PID-Regulator Parameters Optimization

2014 ◽  
Vol 682 ◽  
pp. 80-86 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Control System ◽  
Parameters Optimization ◽  
Rule Base ◽  
Adaptive Control System ◽  
Neural Network Learning ◽  
Network Learning ◽  
Furnace Control ◽  
Network Usage ◽  
Control Modeling

An adaptive control system implementation is described. Such system is based on a neural network, used for online PID-regulator coefficients tuning. The backpropagation online training method is used. It is modified by adding a rule base. It contains conditions on choosing neural network learning rate. PID-regulator with neural tuner and conventional PID-regulator were used as regulators during the process of heating furnace control modeling. Such experiments were made for different loading furnace modes and setpoint schedules. The 11% economy of time on setpoint schedule realizing was achieved with the help of proposed neural tuner..

Neural Network usage in structural crack detection

2015 ◽  
Author(s):  
Mohamed Gaith ◽  
M. El Haj Assad ◽  
Ahmad Sedaghat ◽  
Mohammad Hiyasat ◽  
Saddam Alkhatib
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Network Usage

scholarly journals NEURAL-BASED RECOGNITION OF MULTI-PARAMETER SENSOR SIGNAL DESCRIBED BY MATHEMATICAL MODEL

2014 ◽  
pp. 140-147
Author(s):  
Iryna Turchenko ◽  
Volodymyr Kochan ◽  
Anatoly Sachenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Simulation Modeling ◽  
General Structure ◽  
Signal Recognition ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Sensor Signal ◽  
Network Training ◽  
Network Usage

The possibility of artificial neural network usage for recognition of a signal of a multi-parameter sensor is described in this paper. The general structure of data acquisition channel with usage of neural networks as well as mathematical model of output signal of a multi-parameter sensor is studied in this article. The model of neural network, training algorithm and achieved results of simulation modeling of a multi-parameter sensor signal recognition using MATLAB software are presented at the end of this paper.

scholarly journals Artificial neural network usage for determining solidus temperature of steels

2019 ◽  
Author(s):  
Mario MACHŮ ◽  
Ľubomíra DROZDOVÁ ◽  
Bedřich SMETANA ◽  
Simona ZLÁ ◽  
Monika KAWULOKOVÁ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solidus Temperature ◽  
Network Usage ◽  
Artificial Neural
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