scholarly journals Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

2014 ◽  
Vol 8 ◽  
Author(s):  
Benjamin Dummer ◽  
Stefan Wieland ◽  
Benjamin Lindner
Keyword(s):  
Neural Network ◽  
Power Spectrum ◽  
Spike Train ◽  
Self Consistent ◽  
Sparse Connectivity

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.

Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

2020 ◽  
Author(s):  
CSN Koushik ◽  
Shruti Bhargava Choubey ◽  
Abhishek Choubey ◽  
D. Naresh ◽  
N. Bhanu Prakash Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals Accuracy of Doppler Determinations of Station Positions

1972 ◽  
Vol 48 ◽  
pp. 101-103
Author(s):  
R. J. Anderle
Keyword(s):  
Elevation Angle ◽  
High Elevation ◽  
Time Of Day ◽  
Satellite Observations ◽  
Standard Errors ◽  
Lower Elevation ◽  
Self Consistent

Locations of Doppler satellite observing stations have been revised to obtain a set which is more self-consistent and more consistent with the CIO pole. Residuals of satellite observations for 1970 have been analyzed using the new coordinates to determine mean and standard errors for five days of observations of latitude versus station, time of day, and elevation angle. The accuracy of the determination of latitude is about 4 meters at moderate and high elevation angles. But since more satellite passes occur at lower elevation angles, the accuracy of determination of a component of position based on five days of observation of one satellite is only about 2 meters.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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