X-Ray Image Analysis for the Neural Network-Based Detection of Pathology

Minyazev, R. Sh.; Rumyantsev, A. A.; Dyganov, S. A.; Baev, A. A.

doi:10.3103/s1062873818120134

Cited by 6 publications

(1 citation statement)

References 1 publication

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“…Computational experiments made it possible to determine effective modifications of the original architecture [30]: 10A, 20B, 10C inception blocks; the batch size is 8, the pooling function is MaxPooling and normalization is Softmax, the learning algorithm is Adam, the initial learning rate is 0.0002, and the decay coefficient is 0.7 with an exponential decay function. Dataset splitting: training 35%, validation 15%, testing 50%.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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Assessment of the technical condition of pipelines in polyurethane insulation

Shvetsov

Ziganshin

Zagretdinov

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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The paper considers issues of improving the reliability of operation of pipelines of heating networks. A description of the created diagnostic complex is given, with the help of which it is possible to determine the technical condition of pipelines in polyurethane foam insulation. Finite-element models were constructed and modal calculations of defective and defect-free pipelines in polyurethane foam insulation were carried out. The analysis of the received acoustic signals was carried out using neural network algorithms. The results of processing acoustic vibrations of pipelines by a neural network showed a high efficiency in identifying pipelines by their state (defective / defect-free).

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Convolutional Neural Networks for Predicting Morphological and Nonlinear Optical Properties of Thin Films of Quasi-Two-Dimensional Materials

Popkova,

Fedyanin

2023

Jetp Lett.

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X-Ray Image Analysis for the Neural Network-Based Detection of Pathology

Cited by 6 publications

References 1 publication

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Assessment of the technical condition of pipelines in polyurethane insulation

Convolutional Neural Networks for Predicting Morphological and Nonlinear Optical Properties of Thin Films of Quasi-Two-Dimensional Materials

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