scholarly journals Feature recognition from potential fields using neural networks

1992 ◽  
Author(s):  
Yi Guo ◽  
R. O. Hansen ◽  
Norman Harthill
Keyword(s):  
Neural Networks ◽  
Feature Recognition ◽  
Potential Fields

Algorithms for constructing structural surfaces by geophysical data based on neural networks

2021 ◽  
Author(s):  
Alexey Shklyaruk ◽  
Kirill Kuznetsov ◽  
David Arutyunyan ◽  
Ivan Lygin
Keyword(s):  
Neural Networks ◽  
Sedimentary Cover ◽  
Geophysical Data ◽  
Seismic Exploration ◽  
Potential Fields ◽  
Magnetic Characteristics ◽  
Test Model ◽  
Functional Relationships ◽  
Gravity And Magnetic ◽  
A Site

<p>At the stage of small and medium-scale geological and geophysical studies, in addition to seismic exploration, methods of potential fields (gravimetry and magnetometry) are usually actively used. These methods, in contrast to the profile seismic observations, taking into account modern satellite and aviation technologies, provide a high-quality areal density and magnetic characteristics of the study area. The main tasks of modern gravimetry and magnetometry include the task of constructing areal models, contrasting in density and magnetization of surfaces. Among a large number of algorithmic solutions, the most effective are methods using an integrated approach, in which seismic data on the morphology of reflecting horizon is used as a reference.</p><p>Reconstruction of the structural surface morphology by geophysical data can be considered as the problem of finding the relationship between the input information (potential fields, geophysical data, and available a priori information) and the desired surface. To assess the dependence, it is proposed to use the reference plots on which both input and output data are presented. Currently, one of the trends in solving such problems is methods based on neural networks. Neural networks can be of various configurations (feedforward networks, radial-basis function networks, backpropagation networks, convolutional networks, etc.), have a different number of layers and neurons.</p><p>In this research, we consider the test and real-world example. A site with a known position of the sedimentary cover bottom is considered as a test model. To verify and compare the algorithms, the gravity and magnetic effects of the layer are calculated. The gravity and magnetic fields were supplied to the input to the algorithms for constructing regression dependence and training the neural network. An incomplete model of the sedimentary cover was supplied to the input for training neural networks. The task was to restore the missing part. The parameter of the standard deviation of the original and reconstructed model was less than 2% for all types of neural networks.</p><p>As a real model, a site was considered where basement cover is only partially available. It was obtained as a result of seismic interpretation. All available geological and geophysical data were used to reconstruct the horizon. Models obtained using reconstruction algorithms can be additional information for further detailed description of the geological structure.</p><p>It should be noted that since neural networks help to find complex functional relationships between field parameters and attributes of the studied environment, they could be used in the tasks of complex interpretation of geological and geophysical data.</p>

scholarly journals A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

2020 ◽  
Vol 8 ◽  
Author(s):  
Yue Lin ◽  
Qinghua Zhong ◽  
Hailing Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Feature Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Automatic Identification ◽  
Online Reading ◽  
Reading System

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems

2010 ◽  
Vol 25 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Bojan R. Babić ◽  
Nenad Nešić ◽  
Zoran Miljković
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Feature Recognition ◽  
System Development ◽  
Planning System ◽  
Computer Aided Process Planning ◽  
Design And Manufacturing ◽  
Automatic Feature Recognition ◽  
Feature Technology ◽  
Artificial Neural

AbstractFeature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

scholarly journals Recognition of sign language gestures using neural networks

2007 ◽  
Author(s):  
Simon Vamplew
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Feature Recognition ◽  
Recognition System ◽  
Language Recognition ◽  
Modular Architecture ◽  
Sign Language Recognition ◽  
And Performance ◽  
Nearest Neighbour Classifier ◽  
The University

This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

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