scholarly journals Methods of group classification based on the theory of multisets in the problem of localizing zones with different levels of seismic activity during mining.

2020 ◽  
Vol 11 (8-2020) ◽  
pp. 26-38
Author(s):  
A.A. Zuenko ◽  
◽  
O.V. Fridman ◽  
O.G. Zhuravleva ◽  
S.A. Zhukova ◽  
...  
Keyword(s):  
Neural Network ◽  
Seismic Activity ◽  
Decision Rules ◽  
Group Classification ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Trained Neural Network ◽  
Classification Procedures ◽  
Different Levels

The work is dedicated to assessing the applicability of supervised group classification methods developed on the basis of multiset theory for solving the problem of identifying zones with different degrees of seismic activity (using the example of one of the sections of the highly stressed rock massif of the Kukisvumchorr apatite-nepheline deposit). The initial objects for classification procedures are spatial cells into which the fieldis divided. Each spatial cell is described by a certain set of factors that, according to experts, have an impact on the occurrence of seismic events in a given cell. An original representation of spatial cells (their groups) as a set of multisets is proposed. Studies have been carried out aimed at identifying the influence of various options for presenting the initial data on the result of classification procedures. Representation of objects described by quantitative and / or qualitative features and existing in several versions (copies) in the form of multisets makes it possible not to transform qualitative features into numerical ones when performing clustering procedures and use methods of group classification of objects. Generalized decision rules of group classification for assigning objects (spatial cells) to four classes of seismic hazard are obtained. In contrast to the currently widely used technologies based on the neural network approach, in this work, the training result is not a “black box” in the form of a trained neural network, but a set of rules that can be easily interpreted, which increases the confidence of end users in decision-making procedures.

scholarly journals The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 691 ◽  
Author(s):  
Irina Popova ◽  
Alexandr Rozhnoi ◽  
Maria Solovieva ◽  
Danila Chebrov ◽  
Masashi Hayakawa
Keyword(s):  
Neural Network ◽  
Radio Wave ◽  
Seismic Activity ◽  
Low Frequency ◽  
Magnetic Storms ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Different Types ◽  
Parameters Of Seismicity

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

scholarly journals Neural network principle of implementation of digital filters

2018 ◽  
Vol 193 ◽  
pp. 03052 ◽  
Author(s):  
Sergey Morozov ◽  
Gennady Makarov ◽  
Konstantin Kuzmin
Keyword(s):  
Neural Network ◽  
Digital Filters ◽  
Classical Method ◽  
Signal Spectrum ◽  
Neural Network Approach ◽  
Frequency Responses ◽  
The Neural Network ◽  
Network Methods ◽  
Element Base

Comparative evaluations of the frequency responses (FR) of two types of filters implemented by the classical and neural network methods are carried out. It is shown that the neural network principle of the implementation of digital filters can serve as an alternative to the classical method for specifically defined parameters of FR in the pass bands and attenuation bands of the frequencies of signal spectrum. The simplest method for calculating the parameters of the filters’ difference equations is the neural network approach, regardless of the type of classification of discrete and digital filters. The implementation of TM (transmultiplexer) on a digital element base requires the use of methods of filtering, modulating and demodulating signals that are largely different from traditional analog methods. The frequency responses of non-recursive types of filters presented in the paper are based on the property of the approximable function determined only in the pass bands and attenuation bands of the frequencies of signal spectrum.

Helicopter Gearbox Fault Detection: A Neural Network Based Approach

1999 ◽  
Vol 121 (3) ◽  
pp. 265-272 ◽  
Author(s):  
M. R. Dellomo
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Complete System ◽  
Accelerometer Data ◽  
Network Approach ◽  
Neural Network Approach ◽  
Vibration Measurements ◽  
The Neural Network ◽  
Principal Method

One of the most dangerous problems that can occur in both military and civilian helicopters is the failure of the main gearbox. Currently, the principal method of controlling gearbox failure is to regularly overhaul the complete system. This paper considers the feasibility of using a neural network to perform fault detection on vibration measurements given by accelerometer data. The details and results obtained from studying the neural network approach are presented. Some of the elementary underlying physics will be discussed along with the preprocessing necessary for analysis. Several networks were investigated for detection and classification of the gearbox faults. The performance of each network will be presented. Finally, the network weights will be related back to the underlying physics of the problem.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

3D-3 Classification of Defects for Guided Waves Inspected Pipes by a Neural Network Approach

2007 ◽  
Author(s):  
G. Acciani ◽  
G. Brunetti ◽  
G. Fornarelli ◽  
F. Bertoncini ◽  
M. Raugi ◽  
...  
Keyword(s):  
Neural Network ◽  
Guided Waves ◽  
Network Approach ◽  
Neural Network Approach

Investigation of the possibility of reconstructing strain diagrams using the neural network approach

2011 ◽  
Vol 47 (15) ◽  
pp. 1689-1695
Author(s):  
M. B. Bakirov ◽  
O. A. Mishulina ◽  
I. A. Kiselev ◽  
I. A. Kruglov
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network

Study on Neural Networks Usage to Analyse Correlation between Spectrum of Vibration Acceleration Signal from Pin of Ball Mill and its Filling Level

2015 ◽  
Vol 770 ◽  
pp. 540-546 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Ball Mill ◽  
Signal Amplitude ◽  
Signal Spectrum ◽  
Laboratory Model ◽  
Training Set ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
The Neural Network ◽  
Trained Neural Network

The question about modern intelligent information processing methods usage for a ball mill filling level evaluation is considered. Vibration acceleration signal has been measured on a mill laboratory model for that purpose. It is made with accelerometer attached to a mill pin. The conclusion is made that mill filling level can not be measured with the help of such signal amplitude only. So this signal spectrum processed by a neural network is used. A training set for the neural network is formed with the help of spectral analysis methods. Trained neural network is able to find the correlation between mill pin vibration acceleration signal and mill filling level. Test set is formed from the data which is not included into the training set. This set is used in order to evaluate the network ability to evaluate the mill filling degree. The neural network guarantees no more than 7% error in the evaluation of mill filling level.

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