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Recognizing imperfections with an artificial neural network of a special type.

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Title: Recognizing imperfections with an artificial neural network of a special type.
Authors: Barkhatov, V.1
Source: Russian Journal of Nondestructive Testing. Feb2006, Vol. 42 Issue 2, p92-100. 9p. 8 Diagrams.
Subjects: Artificial neural networks, Nondestructive testing equipment, Engineering instruments, Nondestructive testing, Approximation theory, X-ray microscopes, Testing
Abstract: Recognition of imperfections with the use of signals from nondestructive testing devices is considered. A new type of neural network that allows separation of signals from imperfections of different types is proposed. Concepts of the neural network’s operation are considered. An example of recognition of signals from an ultrasonic flaw detector is given. [ABSTRACT FROM AUTHOR]
Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Recognition of imperfections with the use of signals from nondestructive testing devices is considered. A new type of neural network that allows separation of signals from imperfections of different types is proposed. Concepts of the neural network’s operation are considered. An example of recognition of signals from an ultrasonic flaw detector is given. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – TitleFull: Recognizing imperfections with an artificial neural network of a special type.
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